This dataset shows 2018 individual and family health plans available in the United States where the federal government is operating the Marketplace. States not represented in the dataset runs their own Marketplaces.

This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in the Municipality of São Paulo, Brazil. The interviews focused in particular on the primary health care services covering two of the poorest sub-municipal districts, Cidade Tiradentes and Sapopemba. The Unequal Voices project – Vozes Desiguais in Portuguese – aimed to strengthen the evidence base on the politics of accountability for health equity via multi-level case studies of health systems in Brazil and Mozambique. The project examined the trajectories of change in the political context and in patterns of health inequalities in Brazil and Mozambique, and carried out four case studies to compare the operation of different accountability regimes across the two countries and between different areas within each country. The case studies tracked shifts in accountability relationships among managers, providers and citizens and changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. In each country the research team studied one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services. Health inequities - that is, inequalities in health which result from social, economic or political factors and unfairly disadvantage the poor and marginalised - are trapping millions of people in poverty. Unless they are tackled, the effort to fulfill the promise of universal health coverage as part of the fairer world envisaged in the post-2015 Sustainable Development Goals may lead to more waste and unfairness, because new health services and resources will fail to reach the people who need them most. In Mozambique, for example, the gap in infant mortality between the best-performing and worst-performing areas actually increased between 1997 and 2008, despite improvements in health indicators for the country as a whole. However, while many low- and middle-income countries are failing to translate economic growth into better health services for the poorest, some - including Brazil - stand out as having taken determined and effective action. One key factor that differentiates a strong performer like Brazil from a relatively weak performer like Mozambique is accountability politics: the formal and informal relationships of oversight and control that ensure that health system managers and service providers deliver for the poorest rather than excluding them. Since the mid-1990s, Brazil has transformed health policy to try to ensure that the poorest people and places are covered by basic services. This shift was driven by many factors: by a strong social movement calling for the right to health; by political competition as politicians realised that improving health care for the poor won them votes; by changes to health service contracting that changed the incentives for local governments and other providers to ensure that services reached the poor; and by mass participation that ensured citizen voice in decisions on health priority-setting and citizen oversight of services. However, these factors did not work equally well for all groups of citizens, and some - notably the country's indigenous peoples - continue to lag behind the population as a whole in terms of improved health outcomes. This project is designed to address the ESRC-DFID call's key cross-cutting issue of structural inequalities, and its core research question "what political and institutional conditions are associated with effective poverty reduction and development, and what can domestic and external actors do to promote these conditions?", by comparing the dimensions of accountability politics across Brazil and Mozambique and between different areas within each country. As Mozambique and Brazil seek to implement similar policies to improve service delivery, in each country the research team will examine one urban location with competitive politics and a high level of economic inequality and one rural location where the population as a whole has been politically marginalised and under-provided with services, looking at changes in power relationships among managers, providers and citizens and at changes in health system performance, in order to arrive at a better understanding of what works for different poor and marginalised groups in different contexts. As two Portuguese-speaking countries that have increasingly close economic, political and policy links, Brazil and Mozambique are also well-placed to benefit from exchanges of experience and mutual learning of the kind that Brazil is seeking to promote through its South-South Cooperation programmes. The project will support this mutual learning process by working closely with Brazilian and Mozambican organisations that are engaged in efforts to promote social accountability through the use of community scorecards and through strengthening health oversight committees, and link these efforts with wider networks working on participation and health equity across Southern Africa and beyond. This dataset comprises interviews conducted between 2016 and 2018 with health service users, health professionals and health system managers in the Municipality of São Paulo, Brazil. Interviewee sampling was purposive and made use of snowballing. The interviews focused in particular on the primary health care services covering two of the poorest suprefeituras (sub-municipal districts), Cidade Tiradentes and Sapopemba. The dataset includes a mix of transcripts and summary notes from individual and group interviews. All material is in Portuguese.

This dataset reports the name, street address, city, county, zip code, telephone number, latitude, and longitude of Pennsylvania Department of Drug and Alcohol Programs (DDAP) drug and alcohol treatment facilities in Pennsylvania as of May 2018.

The primary difference between the three types of treatment facilities is their funding. Centers of Excellence (COEs) were grant funded by the Department of Human Services, PacMATs were grant funded by the Department of Health, and all other facilities are funded by either billing insurance or billing the county in the case of uninsured clients.

Programmatically, COEs differ from the other types because they are designed to serve as “health homes” for individuals with Opioid Use Disorder (OUD). This means that the care coordination staff at the COE is charged with coordinating all kinds of health care (physical and behavioral health) as well as recovery support services. They do this by developing hub-and-spoke networks with other healthcare providers and other sources for recovery supports, such as housing, transportation, education and training, etc. All COEs are required to accept Medicaid.

PacMATs also operate in a hub-and-spoke model, but it is different from COEs. PacMATs endeavor to coordinate the provision of Medication Assisted Treatment (MAT) by identifying a core hub of physicians in a health system that work with other providers in the health system (spokes) to train them about the safe and effective provision of MAT so that there are more providers in a health system that are able to confidently prescribe various forms of MAT. I do not know whether all PacMATs are required to accept Medicaid as a term of their receipt of the grant, but I do know that all currently designated PacMATs are health systems that do accept Medicaid. PacMAT services have been advertised as being available to all people regardless of insurance type, so I assume this means they are required to serve Medicaid clients, commercially insured clients, and uninsured clients. In the PacMAT program the Hub is supported right now by grant funding (in the future funding such as a per patient/per month capitated rate) and the spokes bill insurance (both Medicaid and Commercial)

DDAP facilities may also be designated as COEs and/or PacMATs. If they are, it means they applied for a specific grant fund and have committed to carrying out the activities of the grant described above. To be clear, DDAP does not run any treatment facilities; they license them. These can be MAT providers such as methadone clinics, providers of outpatient levels of care (i.e., more traditional drug and alcohol counseling services) or inpatient levels of care, such as residential rehabilitation programs. Every facility is different in terms of the menu of services it provides. Every facility also gets to decide what forms of payment they will accept. Many accept Medicaid, but not all do. Some only accept private commercial insurance. Some accept payment from the county on behalf of uninsured clients. And some charge their clients cash for services.

Abstract

The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized, allowing comparison between and within countries over time.

The Health SDIs include healthcare provider effort, knowledge and ability, and the availability of key inputs (for example, basic equipment, medicines and infrastructure, such as toilets and electricity). The indicators provide a snapshot of the health facility and assess the availability of key resources for providing high quality care.

The Kenya SDI Health survey team visited a sample of 3,098 health facilities across Kenya between March and July 2018. The 2018 Kenya SDI is the largest to date. The survey team collected rosters covering 24,098 workers for absenteeism and assessed 4,499 health workers for competence using patient case simulation.

Geographic coverage

National

Analysis unit

Health facilities and healthcare providers

Universe

All health facilities providing primary-level care

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy for SDI surveys is designed towards attaining indicators that are accurate and representative at the national level, as this allows for proper cross-country (i.e. international benchmarking) and across time comparisons, when applicable. In addition, other levels of representativeness are sought to allow for further disaggregation (rural/urban areas, public/private facilities, subregions, etc.) during the analysis stage.

The sampling strategy for SDI surveys follows a multistage sampling approach. The main units of analysis are facilities (schools and health centers) and providers (health and education workers: teachers, doctors, nurses, facility managers, etc.). The multi-stage sampling approach makes sampling procedures more practical by dividing the selection of large populations of sampling units in a step-by-step fashion. After defining the sampling frame and categorizing it by stratum, a first stage selection of sampling units is carried out independently within each stratum. Often, the primary sampling units (PSU) for this stage are cluster locations (e.g. districts, communities, counties, neighborhoods, etc.) which are randomly drawn within each stratum with a probability proportional to the size (PPS) of the cluster (measured by the location’s number of facilities, providers or pupils). Once locations are selected, a second stage takes place by randomly selecting facilities within location (either with equal probability or with PPS) as secondary sampling units. At a third stage, a fixed number of health and education workers and pupils are randomly selected within facilities to provide information for the different questionnaire modules.

Detailed information about the specific sampling process is available in the associated SDI Country Report included as part of the documentation that accompany these datasets.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Health Survey Questionnaire consists of four modules, plus weights:

Module 1: General Information - Administered to the health facility manager to collect information on equipment, medicines, infrastructure and other facets of the health facility.

Module 2: Provider Absence - A roster of healthcare providers is collected and absence measured.

Module 3: Clinical Vignettes – A selection of providers are given clinical vignettes to measure knowledge of common medical conditions.

Module 4: Public expenditure tracking - Information on facility finances

Weights: Weights for facilities, absentee-related analyses and clinical vignette analyses.

Cleaning operations

Quality control was performed in Stata.

This is a list of active Medicaid fee-for-service (FFS) providers including the provider’s profession or service, MMIS ID, MMIS Name, NPI, county, and state. For more information visit https://www.emedny.org/info/ProviderEnrollment/ManagedCareNetwork/index.aspx.

This dataset publishes for the first time all enrolled providers in the New York State Medicaid fee for service (FFS) program. This will allow patients with Medicaid FFS coverage to search for potential providers who may be accepting Medicaid FFS patients. This dataset will also enable health plans to confirm Medicaid FFS provider enrollment, which is required under federal law. Section 5005(b)(2) of the 21st Century Cures Act amended Section 1932(d) of the Social Security Act (SSA) requiring that, effective January 1, 2018, all Medicaid Managed Care and Children’s Health Insurance Program providers be enrolled with state Medicaid programs.

This publication brings together the Learning Disabilities and Autism (LDA) data from the Assuring Transformation (AT) collection and the LDA service specific statistics from the Mental Health Statistics Data Set (MHSDS). A couple of figures on commissioner counts were corrected in the AT CSV file on 20th May 2021. There are differences in the inpatient figures between the MHSDS and AT data sets and work is underway to better understand these. The MHSDS LDA data are currently labelled experimental as they are undergoing evaluation. Further information on the quality of these statistics is available in the Data Quality section of the main report. There is a slight difference in scope between the two data collections. The MHSDS data is from providers based in England and includes care provided in England but may be commissioned outside England. Whereas the Assuring Transformation data are provided by English commissioners and healthcare will typically be provided in England but also includes data on care commissioned in England and provided elsewhere in the UK. The release comprises: Assuring Transformation Publication. MHSDS LDA Publication: These statistics are derived from submissions made using version 4.1 of the Mental Health Services Dataset (MHSDS). Prior to May 2018 the LDA service specific statistics were included in the main MHSDS publication. MHSDS Multiple Submission Window Model (MSWM) The MHSDS v4.1 data model allows providers to retrospectively submit data for any monthly reporting period until the end of year cut-off as part of the Multiple Submission Window Model (MSWM). So, for 2020-21, providers are able to resubmit data for any previous months until the end of March 2021. (This was possible for the first time in MHSDS v4.0 but just for the end of year submission for March 2020 data). This model allows providers to improve the quality of previous submissions. Historical comparison with previous years should therefore be reviewed in that context. Additional information on the MSWM for MHSDS is available via the link at the bottom of this page (related links). We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the link to the form at the bottom of this page to provide us with any feedback or suggestions for improving the report.

This publication brings together the Learning Disabilities and Autism (LDA) data from the Assuring Transformation (AT) collection and the LDA service specific statistics from the Mental Health Statistics Data Set (MHSDS). There are differences in the inpatient figures between the MHSDS and AT data sets and work is underway to better understand these. The MHSDS LDA data are currently labelled experimental as they are undergoing evaluation. Further information on the quality of these statistics is available in the Data Quality section of the main report. There is a slight difference in scope between the two data collections. The MHSDS data is from providers based in England and includes care provided in England but may be commissioned outside England. Whereas the Assuring Transformation data are provided by English commissioners and healthcare will typically be provided in England but also includes data on care commissioned in England and provided elsewhere in the UK. The release comprises: Assuring Transformation Publication. MHSDS LDA Publication: These statistics are derived from submissions made using version 4.1 of the Mental Health Services Dataset (MHSDS). Prior to May 2018 the LDA service specific statistics were included in the main MHSDS publication. MHSDS Multiple Submission Window Model (MSWM) The MHSDS v4.1 data model allows providers to retrospectively submit data for any monthly reporting period until the end of year cut-off as part of the Multiple Submission Window Model (MSWM). So, for 2020-21, providers are able to resubmit data for any previous months until the end of March 2021. (This was possible for the first time in MHSDS v4.0 but just for the end of year submission for March 2020 data). This model allows providers to improve the quality of previous submissions. Historical comparison with previous years should therefore be reviewed in that context. Additional information on the MSWM for MHSDS is available via the link at the bottom of this page (related links). We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the link to the form at the bottom of this page to provide us with any feedback or suggestions for improving the report.

Sociodemographic characteristics of study participants, Amhara National, Regional State Referral Hospitals, Ethiopia, 2018 (n = 1060).

The current healthcare spending in Ghana was forecast to continuously increase between 2024 and 2029 by in total 1.1 billion U.S. dollars (+33.4 percent). After the fifth consecutive increasing year, the spending is estimated to reach 4.2 billion U.S. dollars and therefore a new peak in 2029. According to Worldbank health spending includes expenditures with regards to healthcare services and goods. The spending refers to current spending of both governments and consumers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the current healthcare spending in countries like Senegal and Ivory Coast.

Example of analysis process of the study of Bernsten et al. 2018.

Change in PCPS-HF, PCDS-HF and PT-HF for each domain.