This data set includes monthly counts and rates (per 1,000 beneficiaries) of behavioral health services, including emergency department services, inpatient services, intensive outpatient/partial hospitalizations, outpatient services, or services delivered through telehealth, provided to Medicaid and CHIP beneficiaries, by state. Users can filter by either mental health disorder or substance use disorder. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating behavioral health services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

This table presents beneficiaries who received a service for a physical health condition among beneficiaries who received a service for a substance use disorder, by physical health condition, 2017-2021.

Some states have serious data quality issues, making the data unusable for identifying this population. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional, Gender, Age, Zip code, Race and ethnicity, Eligibility group code, Enrollment in CMC Plans.

Data from Maryland, Tennessee, and Utah are omitted for the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

This data set includes monthly counts and rates (per 1,000 beneficiaries) of acute care services, including emergency department (ED) visits, inpatient stays, intensive care unit (ICU) stays, and ICU stays that include ventilator use, provided to Medicaid and CHIP beneficiaries, by state. Users can filter to acute care services for any reason, or acute care services for COVID-19.

These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating acute care services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data.

Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

featAuto_measure_compare_download

  Description

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

  Dataset Details

Publisher: Centers for Medicare & Medicaid Services Last Modified: 2025-01-15 Contact: DataConnect Support Team (dataconnectsupport@cms.hhs.gov)

  Source… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/featautomeasurecomparedownload.

This map shows the access to mental health providers in every county and state in the United States according to the 2024 County Health Rankings & Roadmaps data for counties, states, and the nation. It translates the numbers to explain how many additional mental health providers are needed in each county and state. According to the data, in the United States overall there are 319 people per mental health provider in the U.S. The maps clearly illustrate that access to mental health providers varies widely across the country.The data comes from this County Health Rankings 2024 layer. An updated layer is usually published each year, which allows comparisons from year to year. This map contains layers for 2024 and also for 2022 as a comparison.County Health Rankings & Roadmaps (CHR&R), a program of the University of Wisconsin Population Health Institute with support provided by the Robert Wood Johnson Foundation, draws attention to why there are differences in health within and across communities by measuring the health of nearly all counties in the nation. This map's layers contain 2024 CHR&R data for nation, state, and county levels. The CHR&R Annual Data Release is compiled using county-level measures from a variety of national and state data sources. CHR&R provides a snapshot of the health of nearly every county in the nation. A wide range of factors influence how long and how well we live, including: opportunities for education, income, safe housing and the right to shape policies and practices that impact our lives and futures. Health Outcomes tell us how long people live on average within a community, and how people experience physical and mental health in a community. Health Factors represent the things we can improve to support longer and healthier lives. They are indicators of the future health of our communities.Some example measures are:Life ExpectancyAccess to Exercise OpportunitiesUninsuredFlu VaccinationsChildren in PovertySchool Funding AdequacySevere Housing Cost BurdenBroadband AccessTo see a full list of variables, definitions and descriptions, explore the Fields information by clicking the Data tab here in the Item Details of this layer. For full documentation, visit the Measures page on the CHR&R website. Notable changes in the 2024 CHR&R Annual Data Release:Measures of birth and death now provide more detailed race categories including a separate category for ‘Native Hawaiian or Other Pacific Islander’ and a ‘Two or more races’ category where possible. Find more information on the CHR&R website.Ranks are no longer calculated nor included in the dataset. CHR&R introduced a new graphic to the County Health Snapshots on their website that shows how a county fares relative to other counties in a state and nation. Data Processing:County Health Rankings data and metadata were prepared and formatted for Living Atlas use by the CHR&R team. 2021 U.S. boundaries are used in this dataset for a total of 3,143 counties. Analytic data files can be downloaded from the CHR&R website.

This table presents three populations of beneficiaries who could benefit from different levels of integrated care, 2017-2021: (1) beneficiaries who received services for a behavioral health (BH) condition; (2) beneficiaries who received services for a behavioral health condition who also received services for at least one of a number of select physical health (PH) conditions (a subset of population 1); and (3) beneficiaries prescribed medications for substance use disorders who do not have a medical claim for a behavioral health condition (a subset of population 1). Some states have serious data quality issues, making the data unusable for identifying this population. To assess data quality, analysts used measures featured in the DQ Atlas. Data for a state are considered unusable based on DQ Atlas thresholds for the following topics: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Claims Volume - IP, Diagnosis Code - IP, Diagnosis Code - OT, Procedure Codes - OT Professional, Gender, Age, Zip code, Race and ethnicity, Eligibility group code, Enrollment in CMC Plans. Data from Maryland, Tennessee, and Utah are omitted for the tables due to data quality concerns. Maryland was excluded in 2017 due to unusable diagnosis codes in the IP file and the OT file. Tennessee was excluded due to unusable diagnosis codes in the IP file in 2017 - 2019. Utah was excluded due to unusable procedure codes on OT professional claims in 2017 - 2020. In addition, states with a high data quality concern on one or more measures are noted in the table in the "Data Quality" column. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods.

implAuto_measure_backgroundAndMethods

  Description

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

  Dataset Details

Publisher: Centers for Medicare & Medicaid Services Last Modified: 2025-01-15 Contact: DataConnect Support Team (dataconnectsupport@cms.hhs.gov)… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/implautomeasurebackgroundandmethods.

Local, state, tribal, and federal agencies use health insurance coverage data to plan government programs, determine eligibility criteria, and encourage eligible people to participate in health insurance programs. This map shows where those with no health insurance live. Map opens in Houston, TX. Use the bookmarks or search to see other cities. Zoom out to see map render data for counties and states.

The collection comprises preservation-quality files of Minerva output files without needing download terabyte scale images. To view data in a browser follow this link: https://www.cycif.org/data/lin-wang-coy-2021/viz.html. This dataset uses the Minerva Suite, a series of software tools developed by the Laboratory of Systems Pharmacology to visualize multiplexed tissue image data in a web browser. Researchers or pathologists can annotate and describe images for users and users can use zooming and panning features to explore the full resolution images without needing to download multi-GB/TB image files. These annotated and unannotated images are created by uploading quality controlled ome.tiffs and segmentation masks, along with channel metadata and text descriptions, into the Minerva Author tool. These input files produce .json files, an .html file, and hundreds of .jpg pyramid files that make the images browsable online. This dataset uses multiplexed tissue imaging, spatial statistics, and machine learning to identify cell states underlying morphological features of known prognostic significance in colorectal cancer. We find that the necessary spatial analysis requires extended tumor domains, not tissue microarrays or small view-fields. When this condition is met, the data reveal frequent transitions between histological archetypes (tumor grades and morphologies) correlated with molecular gradients. At tumor invasive margins, where tumor, normal, and immune cells compete, localized features in 2D such as tumor buds and mucin pools are seen in 3D to be large connected structures having continuously varying molecular properties. Immunosuppressive cell-cell interactions also exhibit graded variation in type and frequency. Thus, whereas scRNA-Seq emphasizes discrete changes in tumor state, whole-specimen imaging reveals the presence of multi-scale spatial gradients analogous to those in developing tissues.

Feature service with the current Covid-19 infections per 100,000 inhabitants on the German federal states. The service is updated daily with the current case numbers of the Robert Koch Institute.

Data source: Robert Koch Institute Terms of Use: Robert Koch Institute; German Federal Agency for Cartography and Geodesy Source note: Robert Koch-Institute (RKI), dl-en/by-2-0 Disclaimer: "The content made available on the Internet pages of the Robert Koch-Institute is intended solely for the general information of the public, primarily the specialist public". Data protection declaration: "The use of the RKI website is generally possible without disclosing personal data".

prodAuto_measure_compare_download

  Description

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome.

  Dataset Details

Publisher: Centers for Medicare & Medicaid Services Last Modified: 2024-11-07 Contact: DataConnect Support Team (dataconnectsupport@cms.hhs.gov)… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/prodautomeasurecomparedownload.

This data set includes monthly counts and rates (per 1,000 beneficiaries) of behavioral health services, including emergency department services, inpatient services, intensive outpatient/partial hospitalizations, outpatient services, or services delivered through telehealth, provided to Medicaid and CHIP beneficiaries, by state. Users can filter by either mental health disorder or substance use disorder. These metrics are based on data in the T-MSIS Analytic Files (TAF). Some states have serious data quality issues for one or more months, making the data unusable for calculating behavioral health services measures. To assess data quality, analysts adapted measures featured in the DQ Atlas. Data for a state and month are considered unusable if at least one of the following topics meets the DQ Atlas threshold for unusable: Total Medicaid and CHIP Enrollment, Claims Volume - IP, Claims Volume - OT, Diagnosis Code - IP, Diagnosis Code - OT. Please refer to the DQ Atlas at http://medicaid.gov/dq-atlas for more information about data quality assessment methods. Cells with a value of “DQ” indicate that data were suppressed due to unusable data. Some cells have a value of “DS”. This indicates that data were suppressed for confidentiality reasons because the group included fewer than 11 beneficiaries.

The global market size of Medical Suction System is $XX million in 2018 with XX CAGR from 2014 to 2018, and it is expected to reach $XX million by the end of 2024 with a CAGR of XX% from 2019 to 2024.
Global Medical Suction System Market Report 2019 - Market Size, Share, Price, Trend and Forecast is a professional and in-depth study on the current state of the global Medical Suction System industry. The key insights of the report:
1.The report provides key statistics on the market status of the Medical Suction System manufacturers and is a valuable source of guidance and direction for companies and individuals interested in the industry.
2.The report provides a basic overview of the industry including its definition, applications and manufacturing technology.
3.The report presents the company profile, product specifications, capacity, production value, and 2013-2018 market shares for key vendors.
4.The total market is further divided by company, by country, and by application/type for the competitive landscape analysis.
5.The report estimates 2019-2024 market development trends of Medical Suction System industry.
6.Analysis of upstream raw materials, downstream demand, and current market dynamics is also carried out
7.The report makes some important proposals for a new project of Medical Suction System Industry before evaluating its feasibility.
There are 4 key segments covered in this report: competitor segment, product type segment, end use/application segment and geography segment.
For competitor segment, the report includes global key players of Medical Suction System as well as some small players. At least 11 companies are included:
* Atmos Medical
* Smiths Medical
* Olympus Corporation
* Metasys
* Atlas Copco
* Allied Health Care Products
For complete companies list, please ask for sample pages.
The information for each competitor includes:
* Company Profile
* Main Business Information
* SWOT Analysis
* Sales, Revenue, Price and Gross Margin
* Market Share

For product type segment, this report listed main product type of Medical Suction System market
* Product Type I
* Product Type II
* Product Type III

For end use/application segment, this report focuses on the status and outlook for key applications. End users sre also listed.
* Application I
* Application II
* Application III

For geography segment, regional supply, application-wise and type-wise demand, major players, price is presented from 2013 to 2023. This report covers following regions:
* North America
* South America
* Asia & Pacific
* Europe
* MEA (Middle East and Africa)
The key countries in each region are taken into consideration as well, such as United States, China, Japan, India, Korea, ASEAN, Germany, France, UK, Italy, Spain, CIS, and Brazil etc.

Reasons to Purchase this Report:
* Analyzing the outlook of the market with the recent trends and SWOT analysis
* Market dynamics scenario, along with growth opportunities of the market in the years to come
* Market segmentation analysis including qualitative and quantitative research incorporating the impact of economic and non-economic aspects
* Regional and country level analysis integrating the demand and supply forces that are influencing the growth of the market.
* Market value (USD Million) and volume (Units Million) data for each segment and sub-segment
* Competitive landscape involving the market share of major players, along with the new projects and strategies adopted by players in the past five years
* Comprehensive company profiles covering the product offerings, key financial information, recent developments, SWOT analysis, and strategies employed by the major market players
* 1-year analyst support, along with the data support in excel format.
We also can offer customized report to fulfill special requirements of our clients. Regional and Countries report can be provided as well.

This layer shows health insurance coverage by type and by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent uninsured. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B27010 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This map shows where children have no health insurance coverage in the US. Children are defined as those under age 19. The map shows the percentage of all children who are uninsured, but also shows the total count of uninsured children. The map shows uninsured children by states, counties, and tracts, and the map can be customized and saved into a new map for anywhere in the US. The pattern can be seen throughout the US by searching for an area of interest. The data comes from the most current American Community Survey (ACS) estimates from the U.S. Census Bureau. The metadata, vintage, and source information about the data layer used in this map can be found here. The data layer is updated automatically each year when the Census releases their new estimates, so this map always contains the newest data values.To find more US health-related layers and maps to use in your projects, visit the ArcGIS Living Atlas Health subcategory.

This table shows the results of the Health Monitor 2012, a partnership between the GGDs, RIVM and CBS. The Health Monitor provides information about the health and lifestyle of the Dutch population aged 19 and older living in private households. The figures can be broken down by GGD region and municipality. The figures will also be included in RIVM's National Atlas of Public Health. The figures are provided with confidence intervals. In 2012, the regular Statistics Netherlands Health Survey was part of the Health Monitor. For the long-term trends, figures have been published in the trend tables based only on the regular Health Survey. This also concerns topics included in the Health Monitor Table explained here. For more information about the results of the regular Health Survey, see section 3. The figures from the Health Survey and the Health Monitor cannot be directly compared because there are methodological differences that cannot be corrected for. For information at a regional or local level, it is recommended to use the figures from the Health Monitor. For comparisons with other years, it is recommended to use the figures from the tables based on the regular Health Survey (for reasons of comparability over time). Data available on: 2012 Status of the figures: The data are final. Changes as of February 23, 2018: None, this table has been discontinued. When will new numbers come out? Not applicable anymore. This table is monitored by Health Monitor; population aged 19 or older, region, 2016. See section 3.

This study describes a subset of the HNSCC collection on TCIA.

PURPOSE:

Cross sectional imaging is essential for the patient-specific planning and delivery of radiotherapy, a primary determinant of head and neck cancer outcomes. Publicly shared RT data is scarce due to high complexity of RT structure data and the need for registration in time, space, and across planning sets. We here introduce an open access imaging database for patients treated with radiotherapy for head and neck squamous cell carcinoma (HNSCC).

MATERIALS AND METHODS:

2840 consecutive patients with HNSCC treated with curative-intent RT at MD Anderson Cancer Center from 2003 to 2013 were screened. Patients with whole-body PET-CT or abdominal CT scans both before and after RT were included (n=215). Clinical data were retrieved from the MD Anderson Cancer Center custom electronic medical record system, ClinicStation. Using cross sectional imaging, we calculated total body skeletal muscle and adipose content before and after treatment. All files were de-identified and transferred to The Cancer Imaging Archive servers using the RSNA Clinical Trial Processor program. Files were screened for errors or residual PHI using TagSniffer and Posda Tools software, reviewed by TCIA curators, then confirmed at the parent institution.

RESULTS:

The HNSCC collection is a dataset consisting of 433,384 DICOM files from 3,225 series and 765 studies collected from 215 patients, which includes de-identified diagnostic imaging, radiation treatment planning, and follow up imaging. All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival.

CONCLUSION:

Recent advances in data archiving, patient de-identification, and image registration have allowed for the creation of a high quality RT-enriched imaging database within TCIA. Open access to these data allows for interinstitutional comparisons of complete RT details in non-randomized patient populations, allowing for a more granular understanding of three dimensional factors that influence treatment effectiveness and toxicity sparing. A related dataset describing the other component of the HNSCC collection is here: Radiomics outcome prediction in Oropharyngeal cancer DOI: 10.7937/TCIA.2020.2vx6-fy46

prodAuto_files_topicSnapshot

  Description

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome.

  Dataset Details

Publisher: Centers for Medicare & Medicaid Services Last Modified: 2022-10-06 Contact: DataConnect Support Team (dataconnectsupport@cms.hhs.gov)

  Source… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/prodautofilestopicsnapshot.

devAuto_fileType_measureDisplayGroups

  Description

This is a dataset created for use by the DQ Atlas website, and is not intended for use outside that application. For more information on the DQ Atlas and the information contained in this dataset see https://www.medicaid.gov/dq-atlas/welcome

  Dataset Details

Publisher: Centers for Medicare & Medicaid Services Last Modified: 2025-01-15 Contact: DataConnect Support Team (dataconnectsupport@cms.hhs.gov)… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/devautofiletypemeasuredisplaygroups.

Context: The rate of HIV status disclosure to partners is low in Mali, a West African country with a national HIV prevalence of 1.2%. HIV self-testing (HIVST) could increase testing coverage among partners of people living with HIV (PLHIV). The AutoTest-VIH, Libre d'accéder à la connaissance de son Statut (ATLAS) program was launched in West Africa with the objective of distributing nearly half a million HIV self-tests from 2019 to 2021 in Côte d'Ivoire, Mali, and Senegal. The ATLAS program integrates several research activities. This article presents the preliminary results of the qualitative study of the ATLAS program in Mali. This study aims to improve our understanding of the practices, limitations and issues related to the distribution of HIV self-tests to PLHIV so that they can offer the tests to their sexual partners.Methods: This qualitative study was conducted in 2019 in an HIV care clinic in Bamako. It consisted of (i) individual interviews with eight health professionals involved in the distribution of HIV self-tests; (ii) 591 observations of medical consultations, including social service consultations, with PLHIV; (iii) seven observations of peer educator-led PLHIV group discussions. The interviews with health professionals and the observations notes have been subject to content analysis.Results: HIVST was discussed in only 9% of the observed consultations (51/591). When HIVST was discussed, the discussion was almost always initiated by the health professional rather than PLHIV. HIVST was discussed infrequently because, in most of the consultations, it was not appropriate to propose partner HIVST (e.g., when PLHIV were widowed, did not have partners, or had delegated someone to renew their prescriptions). Some PLHIV had not disclosed their HIV status to their partners. Dispensing HIV self-tests was time-consuming, and medical consultations were very short. Three main barriers to HIVST distribution when HIV status had not been disclosed to partners were identified: (1) almost all health professionals avoided offering HIVST to PLHIV when they thought or knew that the PLHIV had not disclosed their HIV status to partners; (2) PLHIV were reluctant to offer HIVST to their partners if they had not disclosed their HIV-positive status to them; (3) there was limited use of strategies to support the disclosure of HIV status.Conclusion: It is essential to strengthen strategies to support the disclosure of HIV+ status. It is necessary to develop a specific approach for the provision of HIV self-tests for the partners of PLHIV by rethinking the involvement of stakeholders. This approach should provide them with training tailored to the issues related to the (non)disclosure of HIV status and gender inequalities, and improving counseling for PLHIV.