Among OECD countries in 2022, South Korea had the highest rate of yearly visits to a doctor per capita. On average, people in South Korea visited the doctors 15.7 times per year in person. Health care utilization is an important indicator of the success of a country’s health care system. There are many factors that affect health care utilization including healthcare structure and the supply of health care providers.

OECD health systems

Healthcare systems globally include a variety of tools for accessing healthcare, including private insurance based systems, like in the U.S., and universal systems, like in the U.K. Health systems have varying costs among the OECD countries. Worldwide, Europe has the highest expenditures for health as a proportion of the GDP. Among all OECD countries, The United States had the highest share of government spending on health care. Recent estimates of current per capita health expenditures showed the United States also had, by far, the highest per capita spending on health worldwide.

Supply of health providers

Globally, the country with the highest physician density is Cuba, although most other countries with high number of physicians to population was found in Europe. The number of graduates of medicine impacts the number of available physicians in countries. Among OECD countries, Latvia had the highest rate of graduates of medicine, which was almost twice the rate of the OECD average.

In 2021, Austria was the country with the highest practicing physician density at 5.4 practicing physicians per thousand population. On the other hand, countries like India, South Africa, and Indonesia had less than one physician per 1,000 population, the lowest physician density among the selected countries.

Medical Doctors in Turkey increased to 2.18 per 1000 people in 2021 from 2.05 per 1000 people in 2020. This dataset includes a chart with historical data for Turkey Medical Doctors.

Throughout the 1970s and 1980s, the Soviet Union consistently had more physicians per 100,000 population than the United States, with the U.S. having roughly two thirds the number of doctors per capita that the USSR had. In real numbers, there were 1.05 million physicians in the Soviet Union in 1985, compared with 0.58 million in the U.S.. In contrast, the U.S. had more dentists per capita than the Soviet Union in these years (which had notoriously bad overall dental care), while the share of nurses was higher in the Soviet Union in the 1970s, but higher in the U.S. in the 1980s. Healthcare in the Soviet Union Despite this relatively large difference in the number of doctors, the death rate in the Soviet Union increased greatly in these years, while it fell in the U.S.. Until the 1970s, healthcare in the Soviet Union had been a centralized system, among the most competent and reliable in the world, and it oversaw significant improvements in the living standards of Soviet citizens while maintaining developmental pace with the west. This system was overhauled in the 1970s, however, and the economic downturn of the following two decades meant that the Soviet healthcare system then deteriorated. Internal standards dropped, less time was spent on patients, and access to medicines (particularly antibiotics) and equipment fell. The supposedly "free" system also became increasingly dominated by under the table payments, where citizens could be expected to pay 500 rubles (2.5 times the average monthly salary) for an operation or baby delivery.

While the number of physicians and hospital beds increased in the 1970s and 1980s, the lack of training saw an overall decline in the standard of healthcare provided. In these decades, a private healthcare system also opened for Soviet elites, and a disproportionate amount of healthcare professionals defected from state-run hospitals. Following Soviet dissolution in the 1990s, attempted reforms in successor states often failed due to economic mismanagement, and the quality of healthcare dropped even further in many areas, before gradually improving in the past two decades.

In Mexico, the number of practicing doctors amounted to around 2.51 professionals per 1,000 inhabitants in 2021, an increase compared to the figures reported a year earlier when there were around 2.41 practicing physicians per every thousand people. During the last year depicted, the number of physicians in Mexico totaled approximately 324,000 professionals.

Density of doctors worldwide

In a global comparison, Mexico ranks in an middle category for density of medical doctors per 1,000 population, similar to Japan and Qatar. Among the countries in the upper bracket for highest density of doctors are Cuba, Georgia, Lithuania, and Greece. Along with Mexico’s moderate density of doctors, over 39 percent of the population was considered vulnerable due to lack of access to health services in Mexico as of 2022, up from around 21.5 percent a decade earlier.

Health care in Mexico

Nearly 33 million people in Mexico held public health insurance through Seguro Popular in 2020, which was replaced by a new institution at the beginning of that year, called INSABI (Instituto Nacional de Salud para el Bienestar). However, the IMSS (Instituto Mexicano del Seguro Social) led by a large margin as the largest provider of health insurance in the North American country.

Finland Population Per Medical Doctors Graduates was up 0.5% in 2019, from a year earlier.

As of 2019, the south Indian state of Kerala had the highest density of doctors of about 42 per ten thousand population in the country. However, Jharkhand had the least density of doctors in the country of about four doctors per ten thousand people in the state.

In 2022, there were approximately 2.41 medical doctors per 1,000 people in Brazil, a decrease compared to the highest physician density of about 2.49 doctors per thousand inhabitants reported two years earlier. That same year, the number of doctors registered in the South American country totaled about 584 thousand professionals, most of them based in São Paulo.

Forecast: Population Per Medical Doctors Graduates in France 2024 - 2028 Discover more data with ReportLinker!

This statistic displays the average physician-to-population ratio in select U.S. metropolitan areas as of 2013. During this year, there was an average of 268.1 physicians per 100,000 population in Detroit. Boston has one of the overall highest average wait times for a physician appointment. The average cumulative wait time is approximately 18.5 days in 2014, which has decreased since 2004.

Forecast: Population Per Medical Doctors Graduates in Italy 2024 - 2028 Discover more data with ReportLinker!

Forecast: Population Per Medical Doctors Graduates in Germany 2024 - 2028 Discover more data with ReportLinker!

In 2021, there were approximately 2.56 doctors for every 1,000 inhabitants in South Korea. This was a slight increase compared to the preceding year. This ratio has increased steadily since 2000, with the exception of a slight drop in 2004. However, South Korea still has few doctors relative to its population. The OECD average was 3.3 doctors per 1,000 people, and among the full OECD member countries, only Mexico and Turkey had lower ratios. According to OECD data, key partners China, Brazil, India, South Africa, and Indonesia have ratios lower than Korea. At the other end of the spectrum is Austria with 5.48 doctors per 1,000 people, followed by Norway with 5.16 and Germany with 4.53. Shortage of medical staffs The Korean government officially declared a shortage of several thousand doctors across the nation and recommended the training of an additional 150 doctors every year to make up the shortfall. Furthermore, doctors of traditional Korean medicine are counted among the number of doctors, meaning there are even fewer doctors of modern medical sciences than official figures suggest. Yet there are several factors, such as resistance from doctors, preventing the government from simply increasing the number of medical graduates. Regional imbalances in the medical environmentSome experts refute the government’s claims that Korea faces a doctor shortage and point towards other factors. For example, Korea has a higher population density than other countries, meaning that the average Korean doctor meets with more patients than an Austrian or Norwegian one would. Indeed, half the population is concentrated in the Seoul Capital Area. Additionally, Koreans see doctors around 19 times a year on average, which is far more frequently than any other OECD nationals. Despite this, Korea spends a lower share of its GDP on medical expenditures than other OECD countries, implying that medical personnel do more work for less financial compensation. Regional disparities where doctors are concentrated in Seoul and other major cities is also an issue. The doctor-to-people ratio in Seoul is higher than the national ratio, and the same as the OECD average. Many argue that a shortage of nurses is a greater concern than an alleged shortage of doctors. While the number of trained nurses is adequate, many leave because of harsh working conditions.

Forecast: Population Per Medical Doctors Graduates in the UK 2024 - 2028 Discover more data with ReportLinker!

The number of active physicians in Poland increased by 0.1 per 1,000 inhabitants (+3 percent) in 2021. Therefore, the number in Poland reached a peak in 2021 with 3.44 per 1,000 inhabitants. Find more key insights for the number of active physicians in countries like Norway, Luxembourg, and Italy.

This dataset provides values for MEDICAL DOCTORS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

As of the end of 2020, there were around 269 practicing physicians per 100,000 inhabitants in Japan, up from approximately 259 in 2018. The total number of physicians engaging in medical care amounted to roughly 339.6 thousand in 2020. Despite keeping the highest numbers and density of hospitals worldwide, Japan is not included in the list of leading countries when it comes to the count and density of physicians. Japanese hospitals are, therefore, repeatedly said to be suffering from the shortage of physicians and other medical staff.

Profile of physicians

In 2020, the average age of all registered physicians in Japan was 50.5 years old, indicating a constant increase in the last two decades. Of the total, around 23 percent were female physicians. Tokushima Prefecture had the highest number of physicians among all 47 prefectures, with about 356.7 physicians available per 100,000 population.

Physicians employed at hospitals

In Japan, medical facilities can be divided into two categories: hospitals and medical clinics. Japanese hospitals have facilities for the admission of 20 or more inpatients, while medical clinics provide a smaller scale of services. In 2020, the number of physicians working at hospitals exceeded 216 thousand, showing a continuous growth in recent years. In terms of specialty, the highest number of physicians, around 22 thousand, were working at internal medicine departments in the hospitals. Roughly 10.5 thousand were specializing in surgery.

Abstract

The policy objective of the Impact Evaluation (IE) is to build evidence on the impact and cost-effectiveness of the proposed Performance-Based-Financing (PBF) project in Tajikistan. More specifically, the IE would seek to ascertain: (i) the impact and cost-effectiveness of the PBF model implemented in Tajikistan; and (ii) whether PBF is more effective or cost-effective if implemented in conjunction with additional low cost interventions (Collaborative Quality Improvement, Citizen Report Cards). The results from the IE will help informing the MOH on whether PBF should be scaled-up to additional PHC level institutions in other regions.

The Collaborative Quality Improvement intervention responds to policy concerns that performance incentives may not produce the desired improvements if providers lack the necessary competencies, data to inform decisions and knowledge. The Citizen Report Card attempts to improve the effectiveness of PBF by strengthening the 'short route of accountability', i.e., by increasing accountability of health facilities to their local constituents. Since PBF, collaborative quality improvement (CQI), and citizen report cards (CRC) have never been implemented in large scale in Tajikistan, it is to be expected that the results from the IE will be useful for designing national PHC policy in Tajikistan, and that they will also contribute to the larger body of knowledge on these interventions.

The IE employs both difference-in-difference and experimental approaches to identify the impact of the different combinations of interventions. Assignment to PBF was not random. Three districts in the Sughd region and 4 districts in the Khatlon region were selected to implement the program. All Rural Health Centers (RHCs) in these seven districts are covered by the program. Nine additional district (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.

Within the chosen 16 districts (treatment and control districts), clusters consisting of a RHC and its subsidiary Health Houses were randomly assigned to implement Collaborative Quality Improvement, Citizen Score Cards, or neither of these two interventions. The randomization was blocked by district. In sum, RHCs were assigned into six study arms.

The goal of the Facility-based survey is to measure multiple dimensions of quality of care and collect detailed information on key aspects of facility functioning.Household surveys are primarily used to measure health service coverage at the population level as well as select health outcome indicators measured through anthropometry or tests. The surveys also collect broader data on the health of the households, health seeking behaviors and barriers to use of health services. In addition, PBF and other administrative data would be used to track outcomes over time in the treatment groups 1-3 (the ones receiving performance-based payments).

Geographic coverage

Selected districts in Sughd and Khatlon regions (provinces) in Tajikistan

At baseline, three districts in the Sughd region and 4 districts in the Khatlon region were selected to implement the program. All Rural Health Centers in these seven districts are covered by the program. Nine additional district (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.

Analysis unit

Household

Kind of data

Sample survey data [ssd]

Sampling procedure

Households in catchment areas of selected Rural Health Centers and affiliated Health Houses were randomly selected to be included in the sample if (1) a household member was pregnant in the two years prior to the survey; (2) a household member is above 40 years old.

Mode of data collection

Computer Assisted Personal Interview [capi]

In 2019, there were nearly 30 active physicians per 10,000 resident population in the United States. This statistic shows the number of active physicians per 10,000 resident population in the United States from 1975 to 2019.

Abstract

The 2018 endline survey of the impact evaluation (IE) for Health Performance-Based Financing (PBF) in Tajikistan sought to ascertain: (i) the impact and cost-effectiveness of the PBF model implemented in Tajikistan; and (ii) whether PBF is more effective or cost-effective if implemented in conjunction with additional low-cost interventions (Collaborative Quality Improvement, Citizen Report Cards). The results from the IE will help inform the Ministry of Health on whether PBF should be scaled-up to additional PHC level institutions in other regions.

The Collaborative Quality Improvement intervention responds to policy concerns that performance incentives may not produce the desired improvements if providers lack the necessary competencies to inform decisions and knowledge. The Citizen Report Card attempts to improve the effectiveness of PBF by strengthening the 'short route' of accountability (e.g., by increasing accountability of health facilities to their local constituents). Since PBF, collaborative quality improvement (CQI), and citizen report cards (CRC) have never been implemented on a large scale in Tajikistan, it is to be expected that the results from the IE will be useful for designing national PHC policy in Tajikistan, and that they will also contribute to the larger body of knowledge on these interventions.

The IE employs both difference-in-difference and experimental approaches to identify the impact of the different combinations of interventions. Assignment to PBF was not random. Three districts in the Sughd region and four districts in the Khatlon region were selected to implement the program. All Rural Health Centers (RHCs) in these seven districts are covered by the program. Nine additional districts (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.

Within the chosen 16 districts (treatment and control districts), clusters consisting of an RHC and its subsidiary Health Houses were randomly assigned to implement Collaborative Quality Improvement, Citizen Score Cards, or neither of these two interventions. The randomization was blocked by district. In sum, RHCs were assigned into six study arms.

The goal of the facility-based survey is to measure multiple dimensions of quality of care and collect detailed information on key aspects of facility functioning. Household surveys are primarily used to measure health service coverage at the population level as well as select health outcome indicators measured through anthropometry or tests. The surveys also collect broader data on the health of the households, health seeking behaviors and barriers to use of health services. In addition, PBF and other administrative data would be used to track outcomes over time in the treatment groups 1-3 (the ones receiving performance-based payments). The endline (follow-up) survey took place three years after project implementation. The survey is largely based on the HRITF instruments that were modified to the Tajik and project context.

Geographic coverage

Three districts in the Sughd region and four districts in the Khatlon region were selected to implement the program. All Rural Health Centers in these seven districts are covered by the program. Nine additional districts (two in Sughd and seven in Khatlon) were selected as control districts. The selection of the control districts was guided by geographical proximity to treatment districts and similarity in terms of number of health facilities and doctors per capita. The districts were also selected such that the number of RHCs in treatment and control groups in each region would be similar.

Analysis unit

Health centers, Health workers, Patients (adults & children) Patient household

Kind of data

Sample survey data [ssd]

Sampling procedure

The major features of the sampling procedure include the following steps (they are discussed in more detail in a copy of the study's report located in "External Resources"):

Health Facilities: 1. Table 6-4 in the study's report presents the number of RHCs selected for the sample for each district. Of the 216 RHC selected for the sample (after randomly excluding some RHCs when the total was not divisible by three), 151 have subsidiary HHs. Forty-three HHs were selected of the sample in Sughd and 107 in Khatlon. 2. While some Rural Health Centers have one or more subsidiary Health Houses in their catchment areas, others do not have any. One Health House from each RHC with subsidiary HHs was to be included in the sample. The selection was random with each health house within a cluster having an identical probability of being chosen. Non-selected health houses were ranked to serve as replacements if the survey cannot be implemented in the selected HHs.

Households: 1. The evaluation relies on two samples of households. As the primary focus of the PBF intervention is on Maternal and Child Health (MCH) services, the main household sample is of households with women who experienced a recent pregnancy. This sample would not be appropriate to study the impact on the coverage of services related to Non-Communicable Diseases (NCD). Therefore, a second sample consists of households with individuals over the age of 40. The household samples are clustered according to the catchment area of each Rural Health Center (and its affiliated health houses).
2. The resulting targeted primary household sample size is of 4,320 households, with 20 in each of the 216 clusters in the six study arms. To be eligible to be included in the household survey sample, households must have had at least one woman aged 15-49 years who has had a child in the preceding three years. The same villages were covered for both the baseline and followed up survey and eligibility was determined at each round by a listing exercise. 3. The resulting targeted sample size for the secondary household sample is 1,584 households, with 22 in each of 72 clusters in two of the six study arms. Eligibility for this sample is determined by an individual over the age of 40 in the household. Eligibility for the two samples is determined by a common listing of households in selected villages. Households which satisfy both eligibility criteria can be randomly selected to count towards the sample size requirements for both. 4. A two-stage cluster sampling methodology was employed to identify random samples. First, villages were randomly selected out of a list of the villages served by each facility. The list was obtained from the MoH. RHCs have either single or multiple villages in their catchment areas while HHs typically serve a single village. If an RHC has at least one affiliated HH, then two villages were selected. One village was directly served by the RHC while the other included in the sub-catchment area of the HH. In each village, 100 households were listed. If the village had over 100 households, a random walk method was used to select the target number. A short questionnaire was conducted at each household to determine households' eligibility for the two samples. From all eligible households, the target sample for each catchment area was selected. In catchment areas in which two villages were included in the sample, half of the households were to be selected from each village.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The Tajikistan Health Results Based Financing Impact Evaluation 2018 - Health Facility Endline Survey includes the following 7 questionnaires.

Facility-Based Surveys: 1. Health facility assessment module 2. Health worker interview module 3. Observation of patient-provider interaction module 4. Patient exit interview modules

Household Survey: 5. Main household questionnaire 6. Women of reproductive age interview questionnaire 7. Adults over 40 years old questionnaire

Response rate

Health Facilities: Of 216 RHCs selected for the impact evaluation, 210 were evaluated at both baseline and follow-up. Six RHCs evaluated at baseline were ineligible for selection at follow-up due to closure or re-registration (either upgraded to a district health center or downgraded to health house). These six RHCs and their respective health house and household enumeration areas were replaced before the start of the follow-up survey. A total of 151 health houses were assessed at baseline, and 150 at follow-up. Eleven health houses were close or re-registered as RHCs. Our analyses treat RHCs and health houses as panel data, where it is assumed the observed facility is measured at both time points. Therefore, both the original units which have been replaced and the replacement are excluded in the subsequent difference-in-difference and cross-sectional analyses.

Health Workers: A total of 1,574 health workers were surveyed in the RHCs included in the analysis sample, 767 at baseline and 807 at follow-up. The average number of health workers fell slightly below the 4 per RHC target, as more remote RHCs did not have four staff members available. In health houses, the two staff per HH was achieved in the baseline sample but narrowly missed in the follow-up survey. Health workers who worked in both the rural health center and health house were treated as RHC employees.

Households: A total of 10,599 households were surveyed across 230 villages in 210 RHC catchment areas, 4910 at baseline and 5689 during follow-up covering 83,803