Description

This Dataset contains details of No of Health workforce per 10000 of population.

Acknowledgements

https://www.who.int/

Photo by Marcelo Leal on Unsplash

This dataset reports the number of midwives per 10,000 population, based on data from the WHO National Health Workforce Accounts (NHWA) platform. It provides a standardised measure of workforce density, reflecting the availability of midwifery services in relation to population size. This indicator is essential for assessing health system capacity, identifying gaps in coverage, and informing policies aimed at equitable access to skilled midwifery care worldwide.Number of midwives (Midwifery Professionals + Midwifery Associate Professionals + Nurse-midwife professionals + Nurse-midwife associate professionals) per 10,000 population. Note: Data doesn't include nurse-midwives.Data Source: WHO national health workforce reporting systems: https://apps.who.int/nhwaportal/Data Dictionary:The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

Information on Medical Facilities in Various Countries

1. Hospital beds (per 10 000 population) 2. Hospital beds (per 10 000 population) 3. Country has national policy or strategy on the use of social media by government organizations 4. Total density per 100 000 population: District/rural hospitals 5. Total density per 100 000 population: Provincial hospitals 6. Total density per 100 000 population, Specialized hospitals 7. Community health workers density (per 10 000 population)

Let me know if there are any other factors we can use, and I will update the column accordingly.

Let's keep the fight up against COVID-19

Data Sources: Global Health Observatory API: https://www.who.int/data/gho

Tasks to do: Append Proper list of Countries as per SpatialDim

BackgroundA global midwifery shortage hampers the goal of ending preventable maternal/newborn mortality and stillbirths. Whether current measures of midwifery workforce adequacy are valid is unknown. We compare two measures of density and distribution of midwifery professionals to assess their consistency, and explore how incorporating midwifery scope, competency, and the adjusting reference population impacts this critical metric.Methods and findingsWe collected a census of midwives employed in eligible facilities in our study settings, (422 in Ghana; 909 in India), assessed the number practicing within the scope of work for midwifery professionals defined in the International Labor Organization International Standard Classification of Occupations, and whether they reported possessing the ICM essential competencies for basic midwifery practice. We altered the numerator, iteratively narrowing it from a simple count to include data on scope of practice and competency and reported changes in value. We altered the denominator by calculating the number of midwives per 10,000 total population, women of reproductive age, pregnancies, and births and explored variation in the indicator. Across four districts in Ghana, density of midwives decreased from 8.59/10,000 total population when counting midwives from facility staffing rosters to 1.30/10,000 total population when including only fully competent midwives by the ICM standard. In India, no midwives met the standard, thus the midwifery density of 1.37/10,000 total population from staffing rosters reduced to 0.00 considering competency. Changing the denominator to births vastly altered subnational measures, ranging from ~1700% change in Tolon to ~8700% in Thiruvallur.ConclusionOur study shows that varying underlying parameters significantly affects the value of the estimate. Factoring in competency greatly impacts the effective coverage of midwifery professionals. Disproportionate differences were noted when need was estimated based on total population versus births. Future research should compare various estimates of midwifery density to health system process and outcome measures.

Perceptions of Healthcare Finance and System Quality Among Nigerian Healthcare Workers

https://doi.org/10.5061/dryad.b8gtht7mn

Description of the data and file structure

• This is the healthcare workers' part of our nationwide research.
• You can open the file using Microsoft Excel, Google Spreadsheet, or any other spreadsheet processor.
• Data Set 1 is the survey responses:
  • This Excel spreadsheet contains the data from a survey that spanned nine states from five geopolitical zones in Nigeria from June to August 2023.
  • It contains participants' responses about their social demographics, perceptions towards the healthcare systems, and their opinions regarding the quality of the healthcare system.

Files and variables

File: Data_1.xlsx

Description:

This is the healthcare workers' part of our nationwide cross-sectional survey on healthcare quality.

Variables:

Variables include the following

• State
• Years...

These datasets were collected to fulfil the requirement of University coursework.

The complete source code and paper are available on GitHub. Click here.

About Dataset

These datasets contain the information of the World Development Indicator (WDI) provided by the world bank, the non-communicable mortality rate, the suicide rate and the number of health workforce data by the World Health Organization (WHO).

Implementation

• Data Cleaning and Preparation
• Exploratory Data Analysis
• Machine Learning Prediction - Random Forest
• Deep Learning Prediction - Neural Network

Long-term quantitative series for 20 Latin American countries, spanning from 1960 to 2020, on the number of hospital beds, physicians, nurses and healthcare expenditure.

Matus-Lopez, M. and Fernández Pérez, P. 2023. "Transformations in Latin American Healthcare: A Retrospective Analysis of Hospital Beds, Medical Doctors, and Nurses from 1960 to 2022". Journal of Evolutionary Studies in Business.

The information was extracted from official reports and cross-country databases. Official reports were available in digital format in the Institutional Repository for Information Sharing (IRIS) of Pan American Health Organization (PAHO). They were summary of four-year reports on Health Conditions in the Americas (PAHO 1962, 1966, 1970, 1974, 1978, 1982, 1986, 1990, 1994, 1998, 2002a), annual reports of Basic Indicators (PAHO 2002b, 2007, 2008, 2010, 2013), Health in South America (PAHO 2012) and Core Indicators (PAHO 2016). Databases were Open Data Portal of the Pan American Health Organization (PLISA) (PAHO 2023), Core Indicator Database provided directly by PAHO (PAHO 2022), Data Portal of National Health Workforce Accounts of the World Health Organization (NHWA) (WHO 2022), and the Global Health Expenditure Database of the World Health Organization (GHED) (WHO 2023).

Serie 1. Hospital Beds per 1,000 inhabitants

Serie 2. Physicians per 10,000 inhabitants

Serie 3. Nurses per 10,000 inhabitants

Serie 4. Government spending on health, per capita. Constant US dollars of 2020

Cite as:

[Переведено с es: испанского языка] Последнее обновление: Jan 7 2024 11:21PM Организация-источник: Глобальная база данных Организации Объединенных Наций по ЦУР [Переведено с en: английского языка] Definition: Health worker densities by occupation Definition: Density of medical doctors: The density of medical doctors is defined as the number of medical doctors, including generalists and specialist medical practitioners per 10,000 population in the given national and/or subnational area. The International Standard Classification of Occupations (ISCO) unit group codes included in this category are 221, 2211 and 2212 of ISCO-08. Density of nursing and midwifery personnel: The density of nursing and midwifery personnel is defined as the number of nursing and midwifery personnel per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2221, 2222, 3221 and 3222. Density of dentists: The density of dentists is defined as the number of dentists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2261. Density of pharmacists: The density of pharmacists is defined as the number of pharmacists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2262. Health worker distribution by sex Percentage of male medical doctors: Male doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212. Percentage of female medical doctors: Female doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212. Percentage of male nursing personnel: Male nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221. Percentage of female nursing personnel: Female nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221. Thematic Area: Sustainable Development Goals Application Area: INDICATOR 3.c.1 Health worker density and distribution Unit of Measurement: Per 10,000 population Data Source: Based on the data from National health Workforce Accounts database, WHO. Available at https://apps.who.int/nhwaportal. For the specific sources and metadata by country, refer to database directly. Last Update: Jan 7 2024 11:21PM Source Organization: United Nations Global SDG Database

BackgroundUnderstanding how governance factors such as democracy and corruption impact the healthcare workforce is crucial for achieving Universal Health Coverage (UHC). Effective health workforce planning and resource allocation are influenced by these political constructs. This study examines the relationship between democracy and corruption and key healthcare workforce metrics.MethodsA cross-sectional study was conducted using a global dataset from 2020 to 2022. The primary outcome was Physician Density (medical doctors per 10000 people). Secondary outcomes included the generalist to specialist ratio and the percentage of female physicians (% Female). Partial correlations, multivariate analysis of variance (MANOVA), and univariate analysis of variance (ANOVA) were used to analyze the relationship between workforce variables and the democracy index (DI), and corruption perception index (CPI), controlling for domestic health expenditure.ResultsData from 134 countries showed significant positive associations between both DI (r = 0.32, p = 0.004) and CPI (r = 0.43, p < 0.001) with physician density. MANOVA indicated significant multivariate effects of DI (Wilks’ Lambda = 0.8642, p = 0.013) and CPI (Wilks’ Lambda = 0.8036, p = 0.001) on the combined workforce variables. Univariate ANOVAs showed that DI (F = 6.13, p = 0.015) and CPI (F = 10.57, p = 0.002) significantly affected physician density, even after adjusting for domestic expenditure (F = 18.53, p < 0.001). However, neither DI nor CPI significantly impacted the Generalist to Specialist Ratio or % Female Physicians.DiscussionHigher levels of democracy and lower levels of corruption are associated with a greater density of medical doctors, independent of healthcare spending. Policymakers must advocate for governance reforms that support a robust healthcare workforce to support aim of universal health coverage.

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

Определение: Плотность медицинских работников в разбивке по профессиям Определение: Плотность врачей: Плотность врачей определяется как количество врачей, включая врачей широкого профиля и врачей-специалистов-практиков, на 10 000 человек населения в данной национальной и/или субнациональной области. В Международной стандартной классификации занятий (ISCO) кодами групп единиц измерения, включенными в эту категорию, являются 221, 2211 и 2212 ISCO-08. Плотность сестринского и акушерского персонала: Плотность сестринского и акушерского персонала определяется как количество сестринского и акушерского персонала на 10 000 человек населения в данной национальной и/или субнациональной области. Коды ISCO-08, включенные в эту категорию, - 2221, 2222, 3221 и 32222. Плотность стоматологов: Плотность стоматологов определяется как количество стоматологов на 10 000 человек населения в данной национальной и/или субнациональной области. Коды ISCO-08, включенные в эту категорию, равны 2261. Плотность фармацевтов: Плотность фармацевтов определяется как количество фармацевтов на 10 000 человек населения в данной национальной и/или субнациональной области. Коды ISCO-08, включенные в эту категорию, равны 2262. Распределение медицинских работников по полу Процент врачей-мужчин: Процент врачей-мужчин от общего числа врачей на национальном уровне. Коды ISCO-08, включенные в эту категорию, - 221, 2211 и 2212. Процентная доля женщин-врачей: Процентная доля женщин-врачей от общего числа врачей на национальном уровне. Коды ISCO-08, включенные в эту категорию, - 221, 2211 и 2212. Процентная доля среднего медицинского персонала мужского пола: Доля среднего медицинского персонала мужского пола от общего числа среднего медицинского персонала на национальном уровне. Коды ISCO-08, включенные в эту категорию, - 2221 и 3221. Процентная доля женского сестринского персонала: Доля женского сестринского персонала в общей численности сестринского персонала на национальном уровне. Коды ISCO-08, включенные в эту категорию, - 2221 и 3221. [Переведено с en: английского языка] Тематическая область: Цели в области устойчивого развития [Переведено с en: английского языка] Область применения: ПОКАЗАТЕЛЬ 3.c.1 Плотность и распределение медицинских работников [Переведено с en: английского языка] Единица измерения: Процент [Переведено с en: английского языка] Источник данных: Национальная база данных о кадрах здравоохранения, ВОЗ. Доступна по адресу https://apps.who.int/nhwaportal. Конкретные источники и метаданные по странам приведены непосредственно в базе данных. [Переведено с es: испанского языка] Последнее обновление: Jan 7 2024 11:31PM Организация-источник: Глобальная база данных Организации Объединенных Наций по ЦУР [Переведено с en: английского языка] Definition: Health worker densities by occupation Definition: Density of medical doctors: The density of medical doctors is defined as the number of medical doctors, including generalists and specialist medical practitioners per 10,000 population in the given national and/or subnational area. The International Standard Classification of Occupations (ISCO) unit group codes included in this category are 221, 2211 and 2212 of ISCO-08. Density of nursing and midwifery personnel: The density of nursing and midwifery personnel is defined as the number of nursing and midwifery personnel per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2221, 2222, 3221 and 3222. Density of dentists: The density of dentists is defined as the number of dentists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2261. Density of pharmacists: The density of pharmacists is defined as the number of pharmacists per 10,000 population in the given national and/or subnational area. The ISCO-08 codes included in this category are 2262. Health worker distribution by sex Percentage of male medical doctors: Male doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212. Percentage of female medical doctors: Female doctors as percentage of all medical doctors at national level. The ISCO-08 codes included in this category are 221, 2211 and 2212. Percentage of male nursing personnel: Male nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221. Percentage of female nursing personnel: Female nursing personnel as percentage of all nursing personnel at national level. The ISCO-08 codes included in this category are 2221 and 3221. Thematic Area: Sustainable Development Goals Application Area: INDICATOR 3.c.1 Health worker density and distribution Unit of Measurement: Percentage Data Source: National health Workforce Accounts database, WHO. Available at https://apps.who.int/nhwaportal. For the specific sources and metadata by country, refer to database directly. Last Update: Jan 7 2024 11:31PM Source Organization: United Nations Global SDG Database

ObjectivesWe contribute a new methodological approach to the ongoing efforts towards evaluating public health surveillance. Specifically, we apply a descriptive framework, grounded in prospect theory (PT), for the evaluation of decisions on disease surveillance deployment. We focus on two attributes of any surveillance system: timeliness, and false positive rate (FPR).MethodsIn a sample of 69 health professionals from a number of health related networks polled online, we elicited PT preferences, specifically respondents’ attitudes towards gains, losses and probabilities (i.e., if they overweight or underweight extreme probabilities) by means of a series of lotteries for either timeliness or FPR. Moreover, we estimated willingness to pay (WTP) for improvements in the two surveillance attributes. For contextualization, we apply our framework to rabies surveillance.ResultsOur data reveal considerable probability weighting, both for gains and losses. In other words, respondents underestimate their chances of getting a good outcome in uncertain situations, and they overestimate their chances of bad outcomes. Moreover, there is convex utility for losses and loss aversion, that is, losses loom larger than gains of the same absolute magnitude to the respondents. We find no differences between the estimated parameters for timeliness and FPR. The median WTP is $7,250 per day gained in detection time and $30 per 1/10,000 reduction in FPR.ConclusionOur results indicate that the biases described by PT are present among public health professionals, which highlights the need to incorporate a PT framework when eliciting their preferences for surveillance systems.

COVID-19 deaths among health care workers, clients and family members due to transmission during access to HIV services and HIV-related deaths that could be averted by these services per 10,000 clients.

NB: The average annual government health expenditure (GHE) per capita is in Ethiopian Birr; the health centres (HCs) in each district are ranked by 15000 population; the health officers, nurses, midwives, and the summation of these professionals together are ranked each by 10000 population; and the average altitudes of the districts in Afar region are retrieved from different sources. (XLSX)