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Coinciding with the ICD-11 2024 release, three new language versions of ICD-11 have been officially launched. 10 languages, Arabic, Chinese, Czech, English, French, Portuguese, Russian, Spanish, Turkish and Uzbek are available, with translations in an additional 25 languages underway.
The ICD-11 2024 version includes over 200 new codes for allergens, providing greater diagnostic detail and precision. In addition, digital tools and APIs have been significantly improved. This release includes the candidate version of the WHO Digital Open Rule Integrated Cause of Death Selection (DORIS) tool, now available in multiple languages, alongside updated APIs. This comprehensive upgrade is expected to improve and strengthen the quality of cause of death information across all member states, supporting better health data management and policy-making.
To facilitate the transition from ICD-10 to ICD-11, WHO has enhanced the digital mapping tables with additional mapping options, offering comprehensive cross-references and guides. These enhancements aim to ensure a smoother and more efficient migration process for all countries.
By 2024, the WHO has made significant progress in linking various medical classifications and terminologies to enhance global health interoperability. This includes lossless mapping of MedDRA (Medical Dictionary for Regulatory Activities) to facilitate accurate reporting of drug-related information, embedding medical device nomenclature for consistency across international health systems, and incorporating Orphanet terminology to improve the classification and understanding of rare diseases. Additionally, WHO is establishing approaches for technical collaboration and linkages with the MONDO Disease Ontology to support accurate disease classification, initiating design efforts with LOINC (Logical Observation Identifiers Names and Codes) to link laboratory and clinical observations with interventions, and exploring potential methods and frameworks for collaboration with other terminology systems to enhance comprehensive health information management.
ICD serves a broad range of uses globally and provides critical knowledge on the extent, causes and consequences of human disease and death worldwide via data that is reported and coded with the ICD. Clinical terms coded with ICD are the main basis for health recording and statistics on disease in primary, secondary and tertiary care, as well as on cause of death certificates. These data and statistics support payment systems, service planning, administration of quality and safety, and health services research. Diagnostic guidance linked to categories of ICD also standardizes data collection and enables large scale research.
For more than a century, the International Classification of Diseases (ICD) has been the basis for comparable statistics on causes of mortality and morbidity between places and over time. Originating in the 19th century, the latest version of the ICD, ICD-11, was adopted by the 72nd World Health Assembly in 2019 and came into effect on 1st January 2022.
Uses of the ICD are diverse and widespread and much of what is known about the extent, causes and consequences of human disease worldwide relies on use of data classified according to ICD. See below just a few examples:
Since the beginning of the pandemic and in response to member state requests, the classification and terminologies unit has been progressively activating emergency codes for COVID-19 in ICD-10 and ICD-11 after consultation with the relevant committees and reference groups of the WHO Family of International Classifications (WHO-FIC) Network.
The Delegates entrusted WHO, as one of its functions, with the task of establishing and revising the necessary international nomenclatures of diseases and causes of death, giving the WorldHealth Assembly authority to adapt regulations in respect, such as nomenclatures, for consideration and action, the International Statistical Classification ofDiseases, Injuries and Causes of Death and accompanying recommendations, destined to improve international uniformity and comparability of statistics of morbidity and mortality.
PMA collects a nationally or sub-nationally representative sample of data from households and women in selected sentinel sites, to estimate family planning and other health indicators on an annual basis in nine pledging FP2020 countries. The PMA surveys involve interviewing a sample of females aged 15 to 49 years and a probability sample of health facilities, pharmacies, and retail outlets that offer family planning services to the selected communities. The female respondents are asked questions about their background, their birth history and fertility preferences, their use of family planning methods, and other information that is helpful to policymakers and program administrators in health and family planning improvement.
The survey sample in each country is based on a multi-stage cluster design, typically using urban-rural and major regions as the strata. A nationally representative number of geographical clusters ("enumeration areas") is sampled in each program country. In each enumeration area, households are listed and mapped. Households are systematically sampled for inclusion in the survey round, using random selection. Embedded in each household survey is the female respondent survey, with a series of questions for all women of reproductive age (15-49) living at each household. Respondents for the service delivery point survey are management staff answering on behalf of the facility.
The household and female surveys are carried out by female data collectors, known as resident enumerators (REs). Eligibility criteria for selection of REs vary by program country. REs are typically women over the age of 21 who are from or near the respective enumeration areas and hold at least a high school diploma.
Each RE takes about six weeks to collect data from all selected households, eligible women, and service delivery points. Data collected from households generate aggregate numbers (descriptive statistics). Data processing produces weighted estimates to report to national and international stakeholders. Data are immediately processed when collection is completed, and the results are disseminated to country stakeholders. Data collection takes place semi-annually in the first two years for each project country and annually thereafter.
PMA uses a two-stage cluster design with typically urban-rural and major regions as the strata. A representative sample of enumeration areas (EAs) are drawn from a master sampling frame covered, usually provided by the national statistical agency in each country. Ahead of data collection, households, and key landmarks in each EA are listed and mapped by resident data collectors. Within each EA at baseline, a random sample of households is selected. The survey aims to include a sample size that would allow analysts to calculate a national estimate for all indicators, including calculating the modern contraceptive prevalence rate (mCPR) with a margin of error of 3 percentage points. The target sample assumed an expected number of eligible women per household and accounted for non-response rates and loss to follow-up. At baseline, all resident eligible females are contacted and consented for interviews. Up to three private SDPs within the EA were also selected for interviews along with the public health posts, district hospitals and regional hospitals serving the EA.
PMA Phase 2 and Phase 3 Survey Protocol >> A summary of Phase 2 and Phase 3 protocols for the household questionnaire, female, service delivery point, and client exit interview surveys. Available in English and French.
PMA Phase 4 Protocol >> A summary of Phase 4 protocols for the household questionnaire, female, service delivery point, and client exit interview surveys. Available in English and French.
PMA2020 Service Delivery Point Sampling Memo >> A summary of PMA2020's general survey sampling strategy including survey sampling assumptions and selection. Available in English and French
PMA2020/Nigeria Sampling Memo >> Summarizes the overall survey design and sample size calculation method of last section provides methods regarding post-stratification weights to calculate national-level estimates, unique for Nigeria PMA2020. Available in English
The PMA platform collects data that are comparable across all program countries and consistent with existing nationally representative surveys. To accomplish this, PMA developed standard household, female, service delivery point, and client exit interview questionnaires. These standard questionnaires are reviewed and modified prior to program launch in each country, to ensure questions are appropriate to each setting. Country-specific questions are also added that reflect programmatic priorities for stakeholders.
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Databases are a requirement for virtually all organizations as a way of storing information digitally, with SQL employed as the main programming language to communicate with and manipulate those databases.
In this course, you will discover how datasets can be explored and manipulated using SQL. You will go from exploring what SQL is and writing your first query to understanding how to produce categorically targeted summary statistics from a large database. Along the way, you will explore a large dataset, filter and group data based on categorical and conditional preferences, and order that data, thereby yielding valuable insights and exemplifying best practices to bring back to your role.
Many different business applications rely on SQL as a backend process to communicate with and manipulate databases, which provides the information and statistics required for related business operations.
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