cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57761/hwzj-cv34
title: India State-Level Disease Burden Initiative

description: The India State-Level Disease Burden Initiative reported an unprecedented comprehensive assessment of the diseases causing the most premature deaths and ill-health in each state of the country, the risk factors responsible for this burden, and their trends spanning 26 years from 1990 to 2016. The burden from 333 disease conditions and injuries and 84 risk factors was computed for each state of India as part of the Global Burden of Disease Study 2016.
description: #Documentation  
Please find the [call for collaborators](<http://www.healthdata.org/gbd/call-for-collaborators>) for more information.



**Methodology**

The India State-Level Disease Burden Initiative was launched in October 2015. It is a collaboration between the Indian Council of Medical Research (ICMR), the Public Health Foundation of India (PHFI), Institute for Health Metrics and Evaluation (IHME), and senior experts and stakeholders currently from about 100 institutions across India. This Initiative reported an unprecedented comprehensive assessment of the diseases causing the most premature deaths and ill-health in each state of the country, the risk factors responsible for this burden, and their trends spanning 26 years from 1990 to 2016. The burden from 333 disease conditions and injuries and 84 risk factors was computed for each state of India as part of the [Global Burden of Disease Study](<http://www.healthdata.org/gbd>) 2016.

The report “[India: Health of the Nation's States — The India State-Level Disease Burden Initiative](<http://www.healthdata.org/policy-report/india-health-nation%E2%80%99s-states>)” details the main findings and is accompanied by the technical paper “[Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study](<http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(17)32804-0/fulltext>)” published in The Lancet and an open access interactive visualization tool [India GBD Compare](<https://vizhub.healthdata.org/gbd-compare/india>) to easily understand the disease burden trends over time across the states.
DOI: 10.57761/as4n-v914
title: Mother and Child Tracking System

description: India’s Mother and Child Tracking System (MCTS) is an information system for tracking maternal and child health beneficiaries in India’s public health system, and improving service delivery planning and outcomes. This ambitious project was launched in 2009 and currently covers all states in India.
The MCTS is designed to capture and track all pregnant women (from conception up to 42 days post-partum) and all new-born children (up to 5 years of age). Its objectives are to ensure that :
- all pregnant women receive their full Antenatal Care (ANC) and Postnatal Care (PNC) services at the due times;
- institutional deliveries for pregnant women, particularly for high risk mothers, are encouraged; 
- and all children receive the full immunisation schedule at the due times.

description: #Documentation  
As the MCTS is an information system for government health operations, it's data is not openly available. Access to the data requires approval of the Ministry of Health. Read more [here](<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4530478/pdf/12913_2015_Article_920.pdf>).



**Methodology**

The Mother and Child Tracking System is a beneficiary- specific database for MCH services delivered through the Indian public health system. It was launched in 2009 as part of a global trend towards harnessing e-health innovations in improving service delivery, and also because India’s existing Health Management Information System (HMIS) was not meeting the service delivery needs of Front Line Health Workers (FHWs).

Beneficiary and service delivery data are written by FHWs on registers and formats and then transferred to the nearest primary health centre (PHC) for entry into the MCTS portal by data entry operators (DEOs). All health facilities, from the state to the most peripheral health sub- centres (HSCs), are mapped in the portal, which also maps FHWs to specific HSCs. The data enables the MCTS to generate workplans for FHWs, detailing forthcoming service delivery needs, such as antenatal check-ups or immunization sessions, on a per-beneficiary basis. Super- visory officials can also generate reports from the MCTS web portal that indicate MCTS performance (beneficiary registration rates) or service delivery performance (e.g. percentage of children fully immunized).
DOI: 10.57761/t373-vs44
title: Ballabgarh Health and Demographic Surveillance System (HDSS)

description: The Ballabgarh Health and Demographic Surveillance System (HDSS), also known as the Comprehensive Rural Health Services Project (CRHSP) Ballabgarh, is located in north India and was established in 1961 to develop a model for rural health-care practice in India. In addition to demographic surveillance and community-based research, CRHSP Ballabgarh provides preventive, health-promotion, and curative services to its surrounding population. Since 1961, CRHSP Ballabgarh has collected demographic data, reproductive data, and health data about mothers and their children. More recently, the project began collecting data on diseases such as tuberculosis, and because of changes in life style it recently began collecting data about non-communicable diseases (NCD) and risk factors for NCDs. 
description: #Documentation  
Collaboration requests can be directed to [CRHSP Ballabgarh](<mailto:crhsp.ballabgarh@gmail.com>), the owner of the data.



**Methodology**

The Comprehensive Rural Health Services Project (CRHSP) Ballabgarh works as a site for community-based research, and functions as a demographic and health surveillance system. This demographic surveillance system has now been in operation for more than 40 years. The population served by CRHSP Ballabgarh in 2011 was about 90,000. The system collects data for the entire population through fortnightly visits by health workers (HWs). The system's data base is updated once every month and further updated with missing data and special morbidity surveillance data during the annual census.

Read more on the website of [INDEPTH](<http://www.indepth-network.org/Profiles/ballabgarh_hdss_2013.pdf>) and in [published work in the International Journal for Epidemiology.](<https://www.ncbi.nlm.nih.gov/pubmed/23620380>)
DOI: 10.57761/8s4w-6z42
title: Global Youth Tobacco Survey

description: The Global Youth Tobacco Survey (GYTS) is a school-based survey designed to enhance the capacity of countries to monitor tobacco use among youth and to guide the implementation and evaluation of tobacco prevention and control programmes. The information generated from the GYTS can be used to stimulate the development of tobacco control programmes and can serve as a means to assess progress in meeting programme goals. In addition, GYTS data can be used to monitor seven Articles in the WHO FCTC.
description: #Documentation  
Please visit [GTSSData](<https://www.cdc.gov/tobacco/global/gtss/gtssdata/index.html>) that houses and displays data from four tobacco-related surveys conducted around the world, including India.





**Methodology**

In December 1998, TFI convened a meeting in Geneva with the Centers for Disease Control and Prevention (CDC), the United Nations Children’s Fund (UNICEF), the World Bank and representatives from countries in each of the six WHO regions to discuss the need for standardized mechanisms to collect youth tobacco use information on a global basis. The outcome of this meeting was the development by WHO and CDC of a Global Tobacco Surveillance System, which uses the Global Youth Tobacco Survey (GYTS) as its data collection mechanism.

The GYTS uses a standard methodology for constructing the sampling frame, selecting schools and classes, preparing questionnaires, following consistent field procedures, and using consistent data management procedures for data processing and analysis.

GYTS is composed of 56 "core" questions designed to gather data on the following seven domains. The questionnaire also allows countries to insert their own country-specific questions.

- Knowledge and attitudes of young people towards cigarette smoking
- Prevalence of cigarette smoking and other tobacco use among young people
- Role of the media and advertising in young people’s use of cigarettes
- Access to cigarettes
- Tobacco-related school curriculum
- Environmental tobacco smoke (ETS)
- Cessation of cigarette smoking

<!-- -->



Centers for Disease Control and Prevention. Global Youth Tobacco Survey Data. Retrieved on 2020 February 21 from [https://nccd.cdc.gov/GTSSDataSurveyResources/Ancillary/DataReports.aspx?CAID=2](<https://nccd.cdc.gov/GTSSDataSurveyResources/Ancillary/DataReports.aspx?CAID=2>)
DOI: 10.57761/5etw-wz74
title: All India Survey on Higher Education (AISHE) 

description: The Ministry of Human Resource Development (MHRD) initiated an All India Survey on Higher Education (AISHE) to build a robust database and to assess the correct picture of higher Education in the country. The main objectives of the survey was to (1) - identify & capture all the institutions of higher learning in the country, (2) - Collect the data from all the higher education institutions on various aspects of higher education.
description: #Documentation  
Please visit [data.gov.in](<https://data.gov.in/catalog/all-india-survey-higher-education-2015-16?filters%5Bfield_catalog_reference%5D=2913321&format=json&offset=0&limit=6&sort%5Bcreated%5D=desc>) and/or contact [Dr N Saravana Kumar.](<mailto:saravana.kumar@gov.in>)



**Methodology**

Data is being collected annually since 2011 on the following broad items

- Institution’s Basic Details 
- Teacher’s Details 
- Details of Non-Teaching Staff 
- Programme conducted under various Faculties/Schools & Departments/Centres
- Students enrolled in these Programme
- Examination result of terminal year of each Programme
- Financial Information such as Receipt and Expenditure under various heads
- Availability of Infrastructure
- Scholarships, Loans & Accreditation

<!-- -->

Data is collected by inviting institutions of higher education to upload information. Around 900 universities, 40.000 colleges and 10.00 stand alone institutions are invited to respond annually (all institutions of higher education in India).
DOI: 10.57761/m26s-1w59
title: PRIME India

description: The Programme for Improving Mental Health Care (PRIME) is creating high quality research evidence on how best to implement and expand the coverage of mental health treatment programmes in low-resource settings. PRIME integrates mental health service delivery into primary health care system in India through a health systems strengthening approach in partnership with researchers, ministries of health and non-governmental organisations.
description: #Documentation  
Please contact[ Gillian Hanslo](<vikram_patel@hms.harvard.edu>) for an expression of interest form.





**Methodology**

The research was conducted in the following stages:

**Inception phase**

During the inception phase (Year 1-2), PRIME developed integrated mental health care plans (MHCPs) comprising packages of mental health care for delivery in primary health care and maternal healthcare suited to each study country’s unique setting.

**Implementation phase**

During the implementation phase (Years 2-5), PRIME implemented these MHCPs in 5 country district sites and evaluated the feasibility, acceptability and impact of the packages of care in primary health care and maternal health care with four separate studies, including a community survey, facility survey, a cohort study and a case study.

**Scale-up phase**

In the scale-up phase (Years 5-7), PRIME scaled up to 94 facilities across the five study countries. The scale-up phase was also evaluated using a case study.

**Extension phase**

During the extension phase (Years 7-8), PRIME is writing up and disseminating its findings, and partnering with other countries beyond the PRIME network. A main goal of PRIME during this final phase is to help make a significant ongoing contribution to a broader investment in mental health and mental healthcare.

Intervention packages are refined for each country and this will include strengthening maternal mental health intervention delivery where required. A key challenge has been ensuring adequate quality of care, and PRIME teams are implementing robust Quality Improvement (QI) measures in all countries.

PRIME teams are also actively working with partners in additional LMICs to assist in implementing PRIME’s approach in other countries. This assistance takes on various forms, including presenting workshops, taking part in meetings to share the tools and manuals PRIME has developed.

**Impact summary**

The PRIME mental healthcare plan has been implemented in 3 facilities and their surrounding communities. By December 2014, 172 service providers working in the district had been trained in the provision of mental healthcare.

In India, no improvement was found in treatment coverage, a small improvement in facility detection and modest improvement in clinical outcomes for individuals with depression and AUD who received care.
DOI: 10.57761/4h0s-2j79
title: US ZIP codes to Census Tracts

description: A crosswalk dataset matching US ZIP codes to corresponding census tracts 
description: #Documentation  
The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

![](<https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368>)*(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)*

**Census tract definition**

A census tract, census area, census district or meshblock is a geographic region defined for the purpose of taking a census. Sometimes these coincide with the limits of cities, towns or other administrative areas and several tracts commonly exist within a county. In unincorporated areas of the United States these are often arbitrary, except for coinciding with political lines.

**Further reading**

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: [ https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf](<https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf>)

**Contact information**

Questions regarding these crosswalk files can be directed to [Alex Din](<mailto:Alexander.M.Din@hud.gov>) with the subject line HUD-Crosswalks.

**Acknowledgement**

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: [https://www.huduser.gov/portal/datasets/usps\_crosswalk.html#codebook](<https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook>)
DOI: 10.57761/5ma3-hv49
title: Andhra Pradesh Children and Parents Study (APCAPS)

description: The Andhra Pradesh Children and Parent Study (APCAPS) is a large prospective, intergenerational cohort study in Southern India that began with the long-term follow-up of the Hyderabad Nutrition Trial (1987-1990).  It is situated in 29 villages near the city of Hyderabad in Ranga Reddy district, Andhra Pradesh.
Extensive data have been collected including: socio-demographics; social position; lifestyle (diet, physical activity, tobacco and alcohol); household characteristics; general health, medical and family history; anthropometric and body composition measures (TANITA and DXA); vascular physiology (BP, arterial stiffness, carotid intima-media thickness), lung function, and fasting biomarkers (e.g. glucose, lipids and insulin). Blood samples and DNA have also been banked.
description: #Documentation  
Please follow instructions for collaboration on the [APCAPS website.](<http://apcaps.lshtm.ac.uk/apply-to-collaborate/>)



**Methodology**

The Hyderabad Nutrition Trial evaluated the Integrated Child Development Services (ICDS) scheme, a national community outreach program, which provides a daily food supplement to pregnant women and children under 6 years of age. The trial used a controlled stepped wedge design, recruiting pregnant women from 29 villages (15 intervention – with program; 14 controls – awaiting implementation) and followed them through to childbirth.

In 2003-5, trial households were retraced and surveyed: families with at least one child born during the trial period and still alive in 2003-05 became the APCAPS prospective cohort (1815 families, 2601 index children). At this time, a first wave (W1) of data collection was carried out on index children and their mothers. The index children were then re-examined as young adults (aged 18-23 years) in 2009-10 (the second wave, W2) and then again in 2010-12 (the third wave, W3) when their siblings and parents were also examined. A socio-demographic household survey of all residents in all 29 villages was completed between 2012 and 2014.

The socio-demographic household survey collected key demographic data on all current residents (n=\~91,000), those who had recently migrated away from the villages (n=\~2,200), and those who had recently died (n=\~1,600). In addition, anthropometric data on children less than six years of age were collected (n=\~7,000), and mothers of these children provided information on their reproductive history and breast-feeding practices
DOI: 10.57761/mk9y-ty94
title: US ZIP codes to CBSA

description: A crosswalk matching US ZIP codes to corresponding CBSA (core-based statistical area)
description: #Documentation  
The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

![](<https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368>)*(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)*

**CBSA definition**

A core-based statistical area (CBSA) is a [U.S.](<https://en.wikipedia.org/wiki/United_States>) geographic area defined by the [Office of Management and Budget](<https://en.wikipedia.org/wiki/Office_of_Management_and_Budget>) (OMB) that consists of one or more counties (or equivalents) anchored by an urban center of at least 10,000 people plus adjacent counties that are socioeconomically tied to the urban center by [commuting](<https://en.wikipedia.org/wiki/Commuting>). Areas defined on the basis of these standards applied to Census 2000 data were announced by OMB in June 2003. These standards are used to replace the definitions of [metropolitan areas](<https://en.wikipedia.org/wiki/Metropolitan_area>) that were defined in 1990. The OMB released new standards based on the 2010 Census on July 15, 2015.

**Further reading**

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: [ https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf](<https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf>)

**Contact authors**

Questions regarding these crosswalk files can be directed to [Alex Din](<mailto:Alexander.M.Din@hud.gov>) with the subject line HUD-Crosswalks.

**Acknowledgement**

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: [https://www.huduser.gov/portal/datasets/usps\_crosswalk.html#codebook](<https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook>)
DOI: 10.57761/fbvb-3b24
title: US ZIP codes to County

description: A crosswalk dataset matching US ZIP codes to corresponding county codes
description: #Documentation  
The denominators used to calculate the address ratios are the ZIP code totals. When a ZIP is split by any of the other geographies, that ZIP code is duplicated in the crosswalk file.

**Example: **ZIP code 03870 is split by two different Census tracts, 33015066000 and 33015071000, which appear in the tract column. The ratio of residential addresses in the first ZIP-Tract record to the total number of residential addresses in the ZIP code is .0042 (.42%). The remaining residential addresses in that ZIP (99.58%) fall into the second ZIP-Tract record.

So, for example, if one wanted to allocate data from ZIP code 03870 to each Census tract located in that ZIP code, one would multiply the number of observations in the ZIP code by the residential ratio for each tract associated with that ZIP code.

![](<https://redivis.com/fileUploads/4ecb405e-f533-4a5b-8286-11e56bb93368>)*(Note that the sum of each ratio column for each distinct ZIP code may not always equal 1.00 (or 100%) due to rounding issues.)*

**County definition**

In the United States, a county is an administrative or political subdivision of a state that consists of a geographic region with specific boundaries and usually some level of governmental authority. The term "county" is used in 48 U.S. states, while Louisiana and Alaska have functionally equivalent subdivisions called parishes and boroughs, respectively.

**Further reading**

The following article demonstrates how to more effectively use the U.S. Department of Housing and Urban Development (HUD) United States Postal Service ZIP Code Crosswalk Files when working with disparate geographies.

Wilson, Ron and Din, Alexander, 2018. “Understanding and Enhancing the U.S. Department of Housing and Urban Development’s ZIP Code Crosswalk Files,” Cityscape: A Journal of Policy Development and Research, Volume 20 Number 2, 277 – 294. URL: [ https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf](<https://www.huduser.gov/portal/periodicals/cityscpe/vol20num2/ch16.pdf>)

**Contact information**

Questions regarding these crosswalk files can be directed to [Alex Din](<mailto:Alexander.M.Din@hud.gov>) with the subject line HUD-Crosswalks.

**Acknowledgement**

This dataset is taken from the U.S. Department of Housing and Urban Development (HUD) office: [https://www.huduser.gov/portal/datasets/usps\_crosswalk.html#codebook](<https://www.huduser.gov/portal/datasets/usps_crosswalk.html#codebook>)