Population Health Management Market size was valued at USD 26.79 Billion in 2023 and is projected to reach USD 77.65 Billion by 2031, growing at a CAGR of 14.23% from 2024 to 2031.

Key Market Drivers • Aging Population and Chronic Disease Management: The growing global older population is pushing the demand for population health management systems to combat chronic diseases. According to the World Health Organization (WHO), the share of the world's population over 60 will nearly double between 2015 and 2050, from 12% to 22%. By 2030, one in every six persons in the world will be 60 or older. This demographic shift is accompanied by a higher frequency of chronic diseases, necessitating more effective community health management strategies. • Rising Healthcare Costs: Healthcare expenses are rising, prompting providers and payers to implement population health management systems for more efficient and cost-effective care delivery.

BackgroundHerpes simplex virus type 1 (HSV-1) commonly causes orolabial ulcers, while HSV-2 commonly causes genital ulcers. However, HSV-1 is an increasing cause of genital infection. Previously, the World Health Organization estimated the global burden of HSV-2 for 2003 and for 2012. The global burden of HSV-1 has not been estimated.MethodsWe fitted a constant-incidence model to pooled HSV-1 prevalence data from literature searches for 6 World Health Organization regions and used 2012 population data to derive global numbers of 0-49-year-olds with prevalent and incident HSV-1 infection. To estimate genital HSV-1, we applied values for the proportion of incident infections that are genital.FindingsWe estimated that 3709 million people (range: 3440–3878 million) aged 0–49 years had prevalent HSV-1 infection in 2012 (67%), with highest prevalence in Africa, South-East Asia and Western Pacific. Assuming 50% of incident infections among 15-49-year-olds are genital, an estimated 140 million (range: 67–212 million) people had prevalent genital HSV-1 infection, most of which occurred in the Americas, Europe and Western Pacific.ConclusionsThe global burden of HSV-1 infection is huge. Genital HSV-1 burden can be substantial but varies widely by region. Future control efforts, including development of HSV vaccines, should consider the epidemiology of HSV-1 in addition to HSV-2, and especially the relative contribution of HSV-1 to genital infection.

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Population Health Management Market Size 2025-2029

The population health management market size is forecast to increase by USD 19.40 billion at a CAGR of 10.7% between 2024 and 2029.

The Population Health Management Market is experiencing significant growth, driven by the increasing adoption of healthcare IT solutions and the rising focus on personalized medicine. The implementation of electronic health records (EHRs) and other digital health technologies has enabled healthcare providers to collect and analyze large amounts of patient data, facilitating proactive care and population health management. Moreover, the trend towards personalized medicine, which aims to tailor healthcare treatments to individual patients based on their unique genetic makeup and health history, is further fueling the demand for PHM solutions. However, the high cost of installing and implementing these platforms poses a significant challenge for market growth.
Despite this, the potential benefits of PHM, including improved patient outcomes, reduced healthcare costs, and enhanced population health, make it an attractive area for investment and innovation. Companies seeking to capitalize on these opportunities must navigate the challenges of data privacy and security, interoperability, and integration with existing healthcare systems. By addressing these challenges and focusing on delivering actionable insights from patient data, PHM solution providers can help healthcare organizations optimize their resources, improve patient care, and ultimately, improve population health.

What will be the Size of the Population Health Management Market during the forecast period?

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The market is experiencing significant growth, driven by the increasing focus on accountable care organizations (ACOs) and payer organizations to improve health outcomes and reduce costs. Healthcare professionals are leveraging big data, data analytics services, and clinical data integration to develop personalized care plans and implement intervention strategies for various populations. Telehealth services have become essential in population health management, enabling care coordination, health promotion, and health navigation for patients. Health equity is a critical factor in population health management, with a growing emphasis on addressing disparities and ensuring equal access to care.
Data security and interoperability standards are essential in population health management, as healthcare providers exchange sensitive patient data for risk adjustment, care pathways, and quality reporting. Data mining and data visualization tools are used to identify health behavior changes and lifestyle modifications, leading to better health outcomes. Consumer health technology, such as patient engagement tools and wearable technology, are playing an increasingly important role in population health management. Health coaching and evidence-based medicine are intervention strategies used to prevent diseases and improve health outcomes. In summary, the market in the US is characterized by the adoption of precision medicine, health literacy, clinical guidelines, and personalized care plans.
The market is driven by the need for care coordination, data analytics, and patient engagement to improve health outcomes and reduce costs. The use of data security, data mining, and interoperability standards ensures the effective exchange and utilization of health data.

How is this Population Health Management Industry segmented?

The population health management industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Software
  Services


End-user

  Large enterprises
  SMEs


Delivery Mode

  On-Premise
  Cloud-Based
  Web-Based
  On-Premise
  Cloud-Based


End-Use

  Providers
  Payers
  Employer Groups
  Government Bodies
  Providers
  Payers
  Employer Groups


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World

By Component Insights

The software segment is estimated to witness significant growth during the forecast period.

The market's software segment is experiencing significant growth and innovation. Healthcare organizations are utilizing these solutions to effectively manage and enhance the health outcomes of diverse populations. The software component incorporates various tools that collect, analyze, and utilize health data for informed decision-making. Population health management platforms gather data from multiple sources, such as electronic health records, claims data, and patient-generated data. These platforms employ advanced analytics to generate valuable insi

Totals may be slightly different due to rounding. Sex-specific estimates for Africa, Eastern Mediterranean, South-East Asia and Western Pacific were generated by applying modelled incidence, calibrated without stratification by sex, to sex-specific population sizes. Region-specific estimates are given to 3 d.p. due to some small numbers of infected individuals, but this level of accuracy is unlikely to be supported by the model.Global and regional estimates of the number of new (incident) cases of HSV-1 infection in 2012 by age and sex, in millions (percentage of population with incident infection shown in parentheses).

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Goal 3Ensure healthy lives and promote well-being for all at all agesTarget 3.1: By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live birthsIndicator 3.1.1: Maternal mortality ratioSH_STA_MORT: Maternal mortality ratioIndicator 3.1.2: Proportion of births attended by skilled health personnelSH_STA_BRTC: Proportion of births attended by skilled health personnel (%)Target 3.2: By 2030, end preventable deaths of newborns and children under 5 years of age, with all countries aiming to reduce neonatal mortality to at least as low as 12 per 1,000 live births and under-5 mortality to at least as low as 25 per 1,000 live birthsIndicator 3.2.1: Under-5 mortality rateSH_DYN_IMRTN: Infant deaths (number)SH_DYN_MORT: Under-five mortality rate, by sex (deaths per 1,000 live births)SH_DYN_IMRT: Infant mortality rate (deaths per 1,000 live births)SH_DYN_MORTN: Under-five deaths (number)Indicator 3.2.2: Neonatal mortality rateSH_DYN_NMRTN: Neonatal deaths (number)SH_DYN_NMRT: Neonatal mortality rate (deaths per 1,000 live births)Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria and neglected tropical diseases and combat hepatitis, water-borne diseases and other communicable diseasesIndicator 3.3.1: Number of new HIV infections per 1,000 uninfected population, by sex, age and key populationsSH_HIV_INCD: Number of new HIV infections per 1,000 uninfected population, by sex and age (per 1,000 uninfected population)Indicator 3.3.2: Tuberculosis incidence per 100,000 populationSH_TBS_INCD: Tuberculosis incidence (per 100,000 population)Indicator 3.3.3: Malaria incidence per 1,000 populationSH_STA_MALR: Malaria incidence per 1,000 population at risk (per 1,000 population)Indicator 3.3.4: Hepatitis B incidence per 100,000 populationSH_HAP_HBSAG: Prevalence of hepatitis B surface antigen (HBsAg) (%)Indicator 3.3.5: Number of people requiring interventions against neglected tropical diseasesSH_TRP_INTVN: Number of people requiring interventions against neglected tropical diseases (number)Target 3.4: By 2030, reduce by one third premature mortality from non-communicable diseases through prevention and treatment and promote mental health and well-beingIndicator 3.4.1: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory diseaseSH_DTH_NCOM: Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)SH_DTH_NCD: Number of deaths attributed to non-communicable diseases, by type of disease and sex (number)Indicator 3.4.2: Suicide mortality rateSH_STA_SCIDE: Suicide mortality rate, by sex (deaths per 100,000 population)SH_STA_SCIDEN: Number of deaths attributed to suicide, by sex (number)Target 3.5: Strengthen the prevention and treatment of substance abuse, including narcotic drug abuse and harmful use of alcoholIndicator 3.5.1: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disordersSH_SUD_ALCOL: Alcohol use disorders, 12-month prevalence (%)SH_SUD_TREAT: Coverage of treatment interventions (pharmacological, psychosocial and rehabilitation and aftercare services) for substance use disorders (%)Indicator 3.5.2: Alcohol per capita consumption (aged 15 years and older) within a calendar year in litres of pure alcoholSH_ALC_CONSPT: Alcohol consumption per capita (aged 15 years and older) within a calendar year (litres of pure alcohol)Target 3.6: By 2020, halve the number of global deaths and injuries from road traffic accidentsIndicator 3.6.1: Death rate due to road traffic injuriesSH_STA_TRAF: Death rate due to road traffic injuries, by sex (per 100,000 population)Target 3.7: By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmesIndicator 3.7.1: Proportion of women of reproductive age (aged 15–49 years) who have their need for family planning satisfied with modern methodsSH_FPL_MTMM: Proportion of women of reproductive age (aged 15-49 years) who have their need for family planning satisfied with modern methods (% of women aged 15-49 years)Indicator 3.7.2: Adolescent birth rate (aged 10–14 years; aged 15–19 years) per 1,000 women in that age groupSP_DYN_ADKL: Adolescent birth rate (per 1,000 women aged 15-19 years)Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for allIndicator 3.8.1: Coverage of essential health servicesSH_ACS_UNHC: Universal health coverage (UHC) service coverage indexIndicator 3.8.2: Proportion of population with large household expenditures on health as a share of total household expenditure or incomeSH_XPD_EARN25: Proportion of population with large household expenditures on health (greater than 25%) as a share of total household expenditure or income (%)SH_XPD_EARN10: Proportion of population with large household expenditures on health (greater than 10%) as a share of total household expenditure or income (%)Target 3.9: By 2030, substantially reduce the number of deaths and illnesses from hazardous chemicals and air, water and soil pollution and contaminationIndicator 3.9.1: Mortality rate attributed to household and ambient air pollutionSH_HAP_ASMORT: Age-standardized mortality rate attributed to household air pollution (deaths per 100,000 population)SH_STA_AIRP: Crude death rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_STA_ASAIRP: Age-standardized mortality rate attributed to household and ambient air pollution (deaths per 100,000 population)SH_AAP_MORT: Crude death rate attributed to ambient air pollution (deaths per 100,000 population)SH_AAP_ASMORT: Age-standardized mortality rate attributed to ambient air pollution (deaths per 100,000 population)SH_HAP_MORT: Crude death rate attributed to household air pollution (deaths per 100,000 population)Indicator 3.9.2: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (exposure to unsafe Water, Sanitation and Hygiene for All (WASH) services)SH_STA_WASH: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (deaths per 100,000 population)Indicator 3.9.3: Mortality rate attributed to unintentional poisoningSH_STA_POISN: Mortality rate attributed to unintentional poisonings, by sex (deaths per 100,000 population)Target 3.a: Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriateIndicator 3.a.1: Age-standardized prevalence of current tobacco use among persons aged 15 years and olderSH_PRV_SMOK: Age-standardized prevalence of current tobacco use among persons aged 15 years and older, by sex (%)Target 3.b: Support the research and development of vaccines and medicines for the communicable and non-communicable diseases that primarily affect developing countries, provide access to affordable essential medicines and vaccines, in accordance with the Doha Declaration on the TRIPS Agreement and Public Health, which affirms the right of developing countries to use to the full the provisions in the Agreement on Trade-Related Aspects of Intellectual Property Rights regarding flexibilities to protect public health, and, in particular, provide access to medicines for allIndicator 3.b.1: Proportion of the target population covered by all vaccines included in their national programmeSH_ACS_DTP3: Proportion of the target population with access to 3 doses of diphtheria-tetanus-pertussis (DTP3) (%)SH_ACS_MCV2: Proportion of the target population with access to measles-containing-vaccine second-dose (MCV2) (%)SH_ACS_PCV3: Proportion of the target population with access to pneumococcal conjugate 3rd dose (PCV3) (%)SH_ACS_HPV: Proportion of the target population with access to affordable medicines and vaccines on a sustainable basis, human papillomavirus (HPV) (%)Indicator 3.b.2: Total net official development assistance to medical research and basic health sectorsDC_TOF_HLTHNT: Total official development assistance to medical research and basic heath sectors, net disbursement, by recipient countries (millions of constant 2018 United States dollars)DC_TOF_HLTHL: Total official development assistance to medical research and basic heath sectors, gross disbursement, by recipient countries (millions of constant 2018 United States dollars)Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basisSH_HLF_EMED: Proportion of health facilities that have a core set of relevant essential medicines available and affordable on a sustainable basis (%)Target 3.c: Substantially increase health financing and the recruitment, development, training and retention of the health workforce in developing countries, especially in least developed countries and small island developing StatesIndicator 3.c.1: Health worker density and distributionSH_MED_DEN: Health worker density, by type of occupation (per 10,000 population)SH_MED_HWRKDIS: Health worker distribution, by sex and type of occupation (%)Target 3.d: Strengthen the capacity of all countries, in particular developing countries, for early warning, risk reduction and management of national and global health risksIndicator 3.d.1: International Health Regulations (IHR) capacity and health emergency preparednessSH_IHR_CAPS: International Health Regulations (IHR) capacity, by type of IHR capacity (%)Indicator 3.d.2: Percentage of bloodstream infections due to selected antimicrobial-resistant organismsiSH_BLD_MRSA: Percentage of bloodstream infection due to methicillin-resistant Staphylococcus aureus (MRSA) among patients seeking care and whose

This dataset presents preliminary findings from the STEPwise Survey, detailing the prevalence of key noncommunicable disease (NCD) risk factors among the population in the State of Qatar. It includes indicators such as hypertension, diabetes, high cholesterol, obesity, and tobacco use.Structured by health indicator, the dataset supports national public health monitoring and the formulation of evidence-based policies for NCD prevention and control. The STEPwise approach, developed by the World Health Organization, provides standardized data to assess population health risks.

Totals may be slightly different due to rounding. Region-specific estimates are given to 3 d.p. due to some small numbers of infected individuals, but this level of accuracy is unlikely to be supported by the model.Global and regional estimates of the number of existing (prevalent) cases of genital HSV-1 infection among 15–49 year olds in 2012 by age and sex, in millions (percentage of population with prevalent infection shown in parentheses), as a function of the assumed proportion of incident HSV-1 infections in this age group that are genital.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

BRIEF FACE-TO-FACE

The metropolitan, urban and rural population and all .administrative regional units. as defined in Official Europe Union Statistics (NUTS 2) covered proportionately the respective population aged 18 and above. The country was divided into an appropriate number of areas, grouping NUTS regions at whatever level appropriately.

The NUTS covered in the Netherlands were the following; Drente, Flevoland, Friesland, Gelderland, Gröningen, Limburg, Noord-Brabant, Noord-Holland, Overijssel, Utrecht, Zeeland, Zuid-Holland.

The basic sample design was a multi-stage, random probability sample. 100 sampling points were drawn with probability proportional to population size, for a total coverage of the country. The sampling points were drawn after stratification by NUTS 2 region and by degree of urbanisation. They represented the whole territory of the country surveyed and are selected proportionally to the distribution of the population in terms of metropolitan, urban and rural areas. In each of the selected sampling points, one address was drawn at random. This starting address forms the first address of a cluster of a maximum of 20 addresses. The remainder of the cluster was selected as every Nth address by standard random route procedure from the initial address. In theory, there is no maximum number of addresses issued per country. Procedures for random household selection and random respondent selection are independent of the interviewer.s decision and controlled by the institute responsible. They should be as identical as possible from to country, full functional equivalence being a must.

At every address up to 4 recalls were made to attempt to achieve an interview with the selected respondent. There was only one interview per household. The final sample size is 1,085 completed interviews.

POSTAL

The Municipal Population Registry (GBA) was used to select a representative sample of 3,000 individuals, aged 18 and over, of the Dutch population. Municipals were selected first and then the individual sample was drawn up.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The metropolitan, urban and rural population and all .administrative regional units. as defined in Official Europe Union Statistics (NUTS 2) covered proportionately the respective population aged 18 and above. The country was divided into an appropriate number of areas, grouping NUTS regions at whatever level appropriately. The NUTS covered in Malta were the following; Inner Harbour Region, Outer Harbour Region, South Eastern Region, Western Region, Northern Region, Gozo and Comino.

The basic sample design was a multi-stage, random probability sample. 50 sampling points were drawn with probability proportional to population size, for a total coverage of the country. The sampling points were drawn after stratification by NUTS 2 region and by degree of urbanisation. They represented the whole territory of the country surveyed and are selected proportionally to the distribution of the population in terms of metropolitan, urban and rural areas.

In each of the selected sampling points, one address was drawn at random. This starting address forms the first address of a cluster of a maximum of 20 addresses. The remainder of the cluster was selected as every Nth address by standard random route procedure from the initial address. In theory, there is no maximum number of addresses issued per country. Procedures for random household selection and random respondent selection are independent of the interviewer's decision and controlled by the institute responsible. They should be as identical as possible from to country, full functional equivalence being a must.

At every address up to 4 recalls are made to attempt to achieve an interview with the selected respondent. There was only one interview per household. The final sample size is 500 completed interviews.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was a multi-stage random probability sample representative of the population residing in urban and rural areas of Jordan. An advanced sample design method in 2 stages was used: 1. Jordan is administratively divided into 12 Governorates, each of which is subdivided into four regions. The survey was carried out in all four regions. 2. Selection of households within the Primary Sampling areas.

The sample structure was based on the estimated population structure elaborated on the basis of the data from the Jordan census of 1994. Statistical data acquired from the Block census had been used in the sample design of this study. The density of the population was classified into three categories: high, medium and low density areas.

The number of sampling units assigned for interviewing per Administrative Unit adequately represented the population density.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was provided by the National Institute of Statistics (DANE) and corresponded to the 1993 national census.

The sample used was a two-stage, non-equal cluster, probabilistic and stratified sample. Thirty-three municipalities were sampled. Some PSUs (Orinoquia, the Amazonian Triangle, and the Pacific Coast - Coast from Nariño, Cauca and Valle, and the state of Chocó- with an urban population that represents less than 2% of the urban population of the country) were eliminated as access was difficult (vast areas, bad roads and high transport cost ).

From the sample of 6,000, females accounted for 65.4% and males for 34.6%.

The major problems reported which affected the response rate were the violence, fear of kidnapping, and political instability.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Rabies Treatment Market Outlook

As of 2023, the global rabies treatment market size is valued at approximately $1.1 billion, with a projected CAGR of 4.5% from 2024 to 2032. By 2032, the market is expected to reach around $1.7 billion, driven by increasing awareness, advancements in vaccine development, and robust government initiatives for disease prevention. The growth in this market is significantly fueled by a rising incidence of rabies cases, particularly in developing regions, and the continuous innovation in treatment methodologies.

One of the primary growth factors in the rabies treatment market is the increased awareness about the severity of the disease and the importance of timely treatment. Public health campaigns and educational programs have played a crucial role in emphasizing the need for vaccination and prompt medical attention post-exposure. These initiatives have led to higher vaccination rates, which, in turn, drive the demand for rabies vaccines and immunoglobulins. Moreover, the expansion of healthcare infrastructure in emerging economies has improved access to treatment, further contributing to market growth.

Advancements in biotechnology and pharmaceutical research are also pivotal in driving the rabies treatment market. Innovative approaches to vaccine development, such as the use of recombinant DNA technology and monoclonal antibodies, are enhancing the efficacy and safety profiles of rabies vaccines and immunoglobulins. Additionally, the ongoing research into novel therapeutic agents, including antiviral drugs and next-generation vaccines, is expected to open new avenues for market growth. These technological advancements are not only improving treatment outcomes but also enabling the development of cost-effective and easily administrable products.

Government initiatives and regulatory support are other significant factors propelling the rabies treatment market. Many countries have implemented national rabies control programs that include mass vaccination campaigns, stray dog population control, and public awareness efforts. International organizations, such as the World Health Organization (WHO) and the World Organisation for Animal Health (OIE), are also actively involved in rabies eradication efforts, providing technical support and funding for global initiatives. These concerted efforts are crucial in reducing the incidence of rabies and driving the demand for treatment products.

The introduction of Vero Cell Rabies Vaccines for Human use has marked a significant advancement in the field of rabies prevention. These vaccines are developed using Vero cell lines, which are known for their safety and efficacy. Unlike traditional vaccines, Vero cell-based vaccines do not require the use of live animals in their production, making them a more ethical and sustainable choice. This innovation not only enhances the safety profile of rabies vaccines but also supports large-scale production, which is crucial in meeting the global demand for rabies prevention. As more countries adopt these vaccines, the accessibility and affordability of rabies vaccination are expected to improve, contributing to the overall reduction in rabies cases worldwide.

Regionally, the rabies treatment market exhibits varying trends and growth rates. In Asia Pacific, the market is experiencing substantial growth due to high incidence rates of rabies and extensive vaccination programs. North America and Europe, while having lower rabies incidence rates, are focusing on maintaining low prevalence through rigorous pre-exposure prophylaxis and post-exposure treatment protocols. Latin America and the Middle East & Africa are also making strides in rabies control, supported by international aid and regional health initiatives. The market dynamics in these regions are influenced by factors such as healthcare infrastructure, availability of vaccines, and public awareness levels.

Product Type Analysis

The rabies treatment market is segmented by product type into vaccines and rabies immunoglobulin. Rabies vaccines dominate the market due to their critical role in both pre-exposure prophylaxis (PrEP) and post-exposure prophylaxis (PEP). These vaccines are essential in preventing the onset of rabies symptoms, which are almost always fatal once they appear. The development of more effective and safer vaccines, including cell culture vaccines and recombinant vaccines, is a significant factor driving market growth. These advancements ensure be

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The country is divided into two regions, the Greater Metropolitan Area and the Rest of the country. The number of interviews in each area was proportionate to its population.

The sampling selection criteria adopted for Costa Rica was that the sampling segments where the interviews were to be held were randomly selected using the maps provided by the National Institute of Statistics and Census of Costa Rica. The procedure took into account the size of the sampling points according to the number of households in each segment (equal selection probability for each household). The resulting sample was self-weighted in terms of geographical population distribution.

General aspects about the sampling strategy in Costa Rica is that 170 different sampling points were selected on a mathematically random basis from the main geographical areas. In each sampling point, eight to ten interviews were conducted. Respondents were selected using the birthday method.

Final sample size=1,508

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was a nationally representative quota sampling of 5,000 respondents. The country was divided into strata provided by the State Planning Organization (SPO). The selection of sampling units was done by demographic variables such as SES, gender, and dwelling.

The sampling frame of the survey corresponded to the index of development of the cities in five strata of SPO; Istanbul, Antalya, Manisa, Trabzon, Yozgat, Adiyaman.

The sampling frame considered gender, dwellings and socioeconomic status. All respondents were identified in terms of socioeconomic status, phone numbers and addresses.

More males (57.2%) than females (42.8%) were interviewed.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

Incontinence Management System Market Outlook

The global incontinence management system market size was valued at approximately USD 12.5 billion in 2023 and is projected to reach USD 21.8 billion by 2032, growing at a CAGR of 6.5% during the forecast period. The market's growth is primarily driven by the increasing prevalence of incontinence disorders, an aging population, and heightened awareness about incontinence management solutions.

The rise in the aging population globally is a significant growth factor for the incontinence management system market. As people age, the likelihood of experiencing incontinence increases, necessitating effective and convenient management systems. According to the World Health Organization, the population aged 60 years and older is expected to reach 2.1 billion by 2050, which underscores the growing demand for incontinence management solutions. Additionally, the advancements in healthcare infrastructure and increased healthcare spending are propelling market growth by making these products more accessible to a larger demographic.

Moreover, the rising awareness about incontinence and its treatments is contributing to market growth. Social stigmas around incontinence have been a barrier in the past; however, increased education and awareness campaigns have helped in mitigating these issues. Organizations and healthcare providers are now more proactive in educating the public about available treatment and management options, thereby driving market demand. Technological advancements in incontinence management systems, such as the development of more comfortable and discreet products, are also playing a crucial role in boosting market growth.

The increasing prevalence of chronic diseases such as diabetes, multiple sclerosis, and Parkinson's disease, which can lead to incontinence, is another factor driving the growth of this market. These conditions often impair the control of bladder and bowel functions, leading to a higher demand for incontinence management products. Additionally, the rise in surgical procedures, which can sometimes result in temporary or permanent incontinence, is also promoting the need for effective management systems. Healthcare providers are increasingly recommending these solutions to improve the quality of life for their patients, further fueling market expansion.

Urinary Incontinence is a prevalent condition that affects millions of individuals worldwide, significantly impacting their quality of life. This condition is characterized by the involuntary leakage of urine, which can occur due to various reasons such as stress, urge, or overflow incontinence. As the population ages, the incidence of urinary incontinence is expected to rise, further driving the demand for effective management solutions. The growing awareness about this condition and the availability of diverse treatment options are encouraging more individuals to seek help, thereby contributing to the growth of the incontinence management system market. Innovations in product design and functionality are also making it easier for individuals to manage urinary incontinence discreetly and comfortably.

Regionally, North America dominates the incontinence management system market, followed by Europe. This dominance is attributed to the high awareness levels, advanced healthcare infrastructure, and the availability of a wide range of products. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to increasing healthcare expenditure, growing awareness, and the rising aging population. Countries like China, Japan, and India are anticipated to be the major contributors to this regional market growth.

Product Type Analysis

In the segment of product type, urinary catheters hold a significant market share due to their widespread use in hospitals and home care settings. Urinary catheters are essential for patients who have urinary retention issues, and technological advancements have led to the development of more comfortable and safer catheters. Additionally, the introduction of antimicrobial-coated catheters has significantly reduced the risk of infections, thereby increasing their adoption. The ease of use and effectiveness of urinary catheters make them a preferred choice among healthcare providers and patients alike.

Absorbent products, including pads and adult diapers, are another crucial segment within the product type category.

Palliative Care Market Outlook

The global palliative care market size was valued at USD 11.2 billion in 2023 and is projected to reach USD 18.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.7% during the forecast period. This growth is primarily driven by the increasing prevalence of chronic diseases and the aging population, alongside rising awareness about the benefits of palliative care.

A significant growth factor for the palliative care market is the rising incidence of chronic illnesses and life-threatening diseases such as cancer, cardiovascular diseases, and chronic respiratory diseases. As these conditions often necessitate long-term care and symptom management, the demand for specialized palliative care services has grown exponentially. The integration of palliative care into standard treatment protocols for chronic diseases has further bolstered market growth. Supportive government policies and funding, aimed at enhancing the quality of life for patients with terminal illnesses, have also played a crucial role in this market's expansion.

The aging population is another crucial factor driving the growth of the palliative care market. Older adults are more likely to suffer from chronic conditions that require comprehensive care management. According to the World Health Organization, the global population aged 60 years and older is expected to double by 2050, reaching approximately 2.1 billion. This demographic shift underscores the importance of palliative care services tailored to meet the unique needs of geriatric patients. Moreover, advancements in medical technology have led to increased life expectancy, thereby augmenting the need for prolonged palliative care services.

Increasing awareness and acceptance of palliative care services among healthcare providers, patients, and families have also contributed significantly to market growth. Educational initiatives and awareness campaigns led by healthcare organizations and non-profits have helped demystify palliative care, highlighting its importance in improving the quality of life for patients with serious illnesses. The emphasis on patient-centered care and the holistic approach of palliative care, which addresses physical, emotional, and spiritual needs, have resonated well with both patients and caregivers. This growing acceptance is expected to continue driving market growth in the coming years.

Health caregiving plays a pivotal role in the realm of palliative care, as it encompasses a wide range of services aimed at supporting patients with serious illnesses and their families. This aspect of care is not limited to medical interventions but extends to emotional, psychological, and spiritual support, ensuring a holistic approach to patient well-being. Health caregiving involves a team of dedicated professionals, including doctors, nurses, social workers, and counselors, who work collaboratively to address the diverse needs of patients. By focusing on the overall quality of life, health caregiving in palliative care helps alleviate the burden of illness, providing comfort and dignity to patients during their most vulnerable times. The growing recognition of the importance of health caregiving in palliative care underscores the need for comprehensive and compassionate services that cater to the unique needs of each patient and their family.

Regionally, North America holds the largest market share, driven by advanced healthcare infrastructure, high healthcare expenditure, and favorable reimbursement policies. Europe follows closely, with significant contributions from countries like Germany, the UK, and France, which have well-established palliative care frameworks. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to the increasing prevalence of chronic diseases, a rapidly aging population, and improving healthcare infrastructure in countries like China and India. Latin America and the Middle East & Africa are also anticipated to experience steady growth, fueled by rising healthcare investments and increasing awareness about palliative care services.

Service Type Analysis

Hospice care is one of the primary service types within the palliative care market, focusing on providing comfort and support to patients in the final stages of a terminal illness. Hospice care services are typically delivered in various settings, including dedicated hospice facilities, hospitals, and patients' home

Medical Dialyzing Paper Market Outlook

The global medical dialyzing paper market size was valued at approximately USD 1.1 billion in 2023 and is projected to reach around USD 2.2 billion by 2032, growing at a CAGR of 7.5% during the forecast period. The market's impressive growth can be attributed to an increasing prevalence of chronic kidney diseases, innovations in dialysis technologies, and rising awareness about renal health management among the global population.

One of the primary growth factors driving the medical dialyzing paper market is the rising incidence of chronic kidney diseases (CKD) globally. According to the World Health Organization (WHO), the prevalence of CKD is expected to rise due to increasing diabetes and hypertension rates, which are major risk factors for kidney dysfunction. As more patients develop CKD, the demand for dialysis procedures, and subsequently dialyzing paper, has surged, driving market growth. Additionally, advancements in medical technology have spurred the development of high-quality dialysis papers that improve filtration efficiency and patient safety, further boosting market demand.

Another significant factor contributing to market growth is the expanding geriatric population. Older adults are more susceptible to renal diseases, necessitating frequent dialysis treatments. The aging population in regions such as North America and Europe is particularly noteworthy, as it directly correlates with an increased demand for dialysis services. Governments and healthcare organizations have been investing in better healthcare infrastructure to cater to this demographic, thereby providing a conducive environment for the growth of the medical dialyzing paper market. Public health initiatives focused on early detection and management of kidney diseases are also supporting market expansion.

Moreover, innovation and technological advancements in dialysis equipment have played a crucial role in the market's growth. Developments such as more efficient dialyzing membranes and biocompatible materials have enhanced the safety and efficacy of dialysis treatments. These advancements have led to the production of medical dialyzing papers that offer better performance, reduced risk of contamination, and improved patient outcomes. The continuous efforts in R&D by leading manufacturers to introduce innovative products are expected to positively influence market dynamics over the forecast period.

Regionally, North America dominates the medical dialyzing paper market due to its advanced healthcare infrastructure, high healthcare expenditure, and significant prevalence of chronic kidney conditions. Europe follows closely, driven by similar factors, along with robust governmental support for healthcare initiatives. The Asia Pacific region is poised for the fastest growth, with countries like China and India investing heavily in healthcare infrastructure and witnessing an increasing burden of kidney diseases. Latin America and the Middle East & Africa are also expected to experience moderate growth, supported by improving healthcare facilities and rising awareness of renal health.

Product Type Analysis

The medical dialyzing paper market can be segmented by product type into single-use dialyzing paper and reusable dialyzing paper. Single-use dialyzing paper is gaining traction due to its convenience, safety, and infection control benefits. The use of single-use products minimizes the risk of cross-contamination between patients, a significant concern in healthcare settings. Given the increasing emphasis on patient safety and infection control protocols, hospitals and dialysis centers are progressively adopting single-use dialyzing papers. This trend is particularly prominent in developed regions where healthcare standards are stringent.

On the other hand, reusable dialyzing paper is favored in healthcare settings where cost-efficiency is a primary concern. These papers can be sterilized and used multiple times, making them a cost-effective solution for dialysis centers and clinics operating under budget constraints. However, the use of reusable papers necessitates stringent sterilization protocols to prevent infections, which can be a challenge in regions with limited access to advanced sterilization equipment. Despite this, in developing regions, reusable dialyzing papers remain a viable option due to their lower overall cost.

The single-use dialyzing paper segment is expected to witness higher growth during the forecast period, driven by advancements in material science that enhance the per

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.

ODS / Goals and targets (from the 2030 Agenda for Sustainable Development) / Goal 3. Ensure healthy lives and promote well-being for all at all ages / Target 3.a. Strengthen the implementation of the World Health Organization Framework Convention on Tobacco Control in all countries, as appropriate. / Indicator 3.a.1. Age-standardized prevalence of current tobacco use among persons aged 15 years and older