3 datasets found
  1. Dataset of Global Religious Composition Estimates for 2010 and 2020

    • pewresearch.org
    Updated 2025
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    Conrad Hackett; Marcin Stonawski; Yunping Tong; Stephanie Kramer; Anne Fengyan Shi (2025). Dataset of Global Religious Composition Estimates for 2010 and 2020 [Dataset]. http://doi.org/10.58094/vhrw-k516
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    Dataset updated
    2025
    Dataset provided by
    Pew Research Centerhttp://pewresearch.org/
    datacite
    Authors
    Conrad Hackett; Marcin Stonawski; Yunping Tong; Stephanie Kramer; Anne Fengyan Shi
    License

    https://www.pewresearch.org/about/terms-and-conditions/https://www.pewresearch.org/about/terms-and-conditions/

    Dataset funded by
    Pew Charitable Trusts
    John Templeton Foundation
    Description

    This dataset describes the world’s religious makeup in 2020 and 2010. We focus on seven categories: Christians, Muslims, Hindus, Buddhists, Jews, people who belong to other religions, and those who are religiously unaffiliated. This analysis is based on more than 2,700 sources of data, including national censuses, large-scale demographic surveys, general population surveys and population registers. For more information about this data, see the associated Pew Research Center report "How the Global Religious Landscape Changed From 2010 to 2020."

  2. i

    Mlomp HDSS INDEPTH Core Dataset 1985 - 2014 (Release 2017) - Senegal

    • datacatalog.ihsn.org
    • catalog.ihsn.org
    Updated Sep 19, 2018
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    Valérie Delaunay (2018). Mlomp HDSS INDEPTH Core Dataset 1985 - 2014 (Release 2017) - Senegal [Dataset]. https://datacatalog.ihsn.org/catalog/7294
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    Dataset updated
    Sep 19, 2018
    Dataset provided by
    Valérie Delaunay
    Cheikh Sokhna
    Gilles Pison
    Laurence Fleury
    El-Hadji Ciré Konko Bâ
    Time period covered
    1985 - 2014
    Area covered
    Senegal
    Description

    Abstract

    In 1985 the population and health observatory was established at Mlomp, in the region of Ziguinchor, in southern Senegal (see map). The objective was to complement the two rural population observatories then existing in the country, Bandafassi, in the south-east, and Niakhar, in the centre-west, with a third observatory in a region - the south-west of the country (Casamance) - whose history, ethnic composition and economic situation were quite different from those of the regions where the first two observatories were located. It was expected that measuring the demographic levels and trends on those three sites would provide better coverage of the demographic and epidemiological diversity of the country.

    Following a population census in 1984-1985, demographic events and causes of death have been monitored yearly. During the initial census, all women were interviewed concerning the birth and survival of their children. Since 1985, yearly censuses, usually conducted in January-February, have been recording demographic data, including all births, deaths, and migrations. The completeness and accuracy of dates of birth and death are cross-checked against those of registers of the local maternity ward (_95% of all births) and dispensary (all deaths are recorded, including those occurring outside the area), respectively. The study area comprises 11 villages with approximately 8000 inhabitants, mostly Diola. Mlomp is located in the Department of Oussouye, Region of Ziguinchor (Casamance), 500 km south of Dakar.

    On 1 January 2000 the Mlomp area included a population of 7,591 residents living in 11 villages. The population density was 108 people per square kilometre. The population belongs to the Diola ethnic group, and the religion is predominantly animist, with a large minority of Christians and a few Muslims. Though low, the educational level - in 2000, 55% of women aged 15-49 had been to school (for at least one year) - is definitely higher than at Bandafassi. The population also benefits from much better health infrastructure and programmes. Since 1961, the area under study has been equipped with a private health centre run by French Catholic nurses and, since 1968, a village maternity centre where most women give birth. The vast majority of the children are totally immunized and involved in a growth-monitoring programme (Pison et al.,1993; Pison et al., 2001).

    Geographic coverage

    The Mlomp DSS site, about 500 km from the capital, Dakar, in Senegal, lies between latitudes 12°36' and 12°32'N and longitudes 16°33' and 16°37'E, at an altitude ranging from 0 to 20 m above sea level. It is in the region of Ziguinchor, Département of Oussouye (Casamance), in southwest Senegal. It is locates 50 km west of the city of Ziguinchor and 25 kms north of the border with Guinea Bissau. It covers about half the Arrondissement of Loudia-Ouolof. The Mlomp DSS site is about 11 km × 7 km and has an area of 70 km2. Villages are households grouped in a circle with a 3-km diameter and surrounded by lands that are flooded during the rainy season and cultivated for rice. There is still no electricity.

    Analysis unit

    Individual

    Universe

    At the census, a person was considered a member of the compound if the head of the compound declared it to be so. This definition was broad and resulted in a de jure population under study. Thereafter, a criterion was used to decide whether and when a person was to be excluded or included in the population.

    A person was considered to exit from the study population through either death or emigration. Part of the population of Mlomp engages in seasonal migration, with seasonal migrants sometimes remaining 1 or 2 years outside the area before returning. A person who is absent for two successive yearly rounds, without returning in between, is regarded as having emigrated and no longer resident in the study population at the date of the second round. This definition results in the inclusion of some vital events that occur outside the study area. Some births, for example, occur to women classified in the study population but physically absent at the time of delivery, and these births are registered and included in the calculation of rates, although information on them is less accurate. Special exit criteria apply to babies born outside the study area: they are considered emigrants on the same date as their mother.

    A new person enters the study population either through birth to a woman of the study population or through immigration. Information on immigrants is collected when the list of compounds of a village is checked ("Are there new compounds or new families who settled since the last visit?") or when the list of members of a compound is checked ("Are there new persons in the compound since the last visit?"). Some immigrants are villagers who left the area several years before and were excluded from the study population. Information is collected to determine in which compound they were previously registered, to match the new and old information.

    Information is routinely collected on movements from one compound to another within the study area. Some categories of the population, such as older widows or orphans, frequently move for short periods of time and live in between several compounds, and they may be considered members of these compounds or of none. As a consequence, their movements are not always declared.

    Kind of data

    Event history data

    Frequency of data collection

    One round of data collection took place annually, except in 1987 and 2008.

    Sampling procedure

    No samplaing is done

    Sampling deviation

    None

    Mode of data collection

    Proxy Respondent [proxy]

    Research instrument

    List of questionnaires: - Household book (used to register informations needed to define outmigrations) - Delivery questionnaire (used to register information of dispensaire ol mlomp) - New household questionnaire - New member questionnaire - Marriage and divorce questionnaire - Birth and marital histories questionnaire (for a new member) - Death questionnaire (used to register the date of death)

    Cleaning operations

    On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

    No imputations were done on the resulting micro data set, except for:

    a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an out-migration event (OMG)

    In the case of the village that was added (enumerated) in 2006, some individuals may have outmigrated from the original surveillance area and setlled in the the new village prior to the first enumeration. Where the records of such individuals have been linked, and indivdiual can legitmately have and outmigration event (OMG) forllowed by and enumeration event (ENU). In a few cases a homestead exit event (EXT) was followed by an enumeration event in these cases. In these instances the EXT events were changed to an out-migration event (OMG).

    Response rate

    On an average the response rate is about 99% over the years for each round.

    Sampling error estimates

    Not applicable

    Data appraisal

    CenterId Metric Table QMetric Illegal Legal Total Metric Rundate
    SN012 MicroDataCleaned Starts 18756 2017-05-19 00:00
    SN012 MicroDataCleaned Transitions 0 45136 45136 0 2017-05-19 00:00
    SN012 MicroDataCleaned Ends 18756 2017-05-19 00:00
    SN012 MicroDataCleaned SexValues 38 45098 45136 0 2017-05-19 00:00
    SN012 MicroDataCleaned DoBValues 204 44932 45136 0 2017-05-19 00:00

  3. f

    Datasets used in the study.

    • plos.figshare.com
    xlsx
    Updated May 15, 2025
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    Abdulsamad Salihu; Ibrahim Jahun; David Olusegun Oyedeji; Wole Fajemisin; Omokhudu Idogho; Samira Shehu; Jennifer Anyanti (2025). Datasets used in the study. [Dataset]. http://doi.org/10.1371/journal.pone.0319807.s001
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    xlsxAvailable download formats
    Dataset updated
    May 15, 2025
    Dataset provided by
    PLOS ONE
    Authors
    Abdulsamad Salihu; Ibrahim Jahun; David Olusegun Oyedeji; Wole Fajemisin; Omokhudu Idogho; Samira Shehu; Jennifer Anyanti
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundHIV infection remains one of the major diseases of public health importance globally with an estimated 40.4 million deaths and 39 million people living with the virus by 2022. About 40 countries are on track to achieve a 95% reduction in AIDS-related mortality by 2030. This progress is however challenged by sub-optimal progress among affected populations (AP), also known as key populations (AP). Society for Family Health (SFH), with about 3 decades of experiences in AP program present in this paper an account of key strategies and innovations in adapting its service provisioning efforts to rapidly changing socio-cultural and political barriers to service delivery among AP in northern Nigeria.MethodsSFH is an indigenous nonprofit, non-political, non-governmental organization in Nigeria that has pioneered HIV interventions among AP across most parts of Nigeria. SFH has successfully tailored its interventions to the unique cultural and religious diversity of Nigeria. The predominantly Islamic-orientated population in the northern part of the country and the Christian-oriented population in the southern part, which is culturally inclined to Western orientations, have all been considered in SFH’s comprehensive approach instilling confidence in the effectiveness of its strategies. SFH implemented 3 key strategies to circumvent pervasive socio-cultural and political barriers that hindered successful AP program implementation in northern Nigeria by addressing structural barriers, systems barriers (service-provider and client-related barriers) and by deployment of innovations to optimize program performance. For the purposes of this retrospective cross-sectional study, deidentified routine aggregate program data was utilized to conduct secondary data analysis.ResultsBetween 2019 – 2023, SFH tested a total of 324,391 AP of whom 30,581 were found to be HIV positives yielding overall positivity rate of 9.4%. People who inject drugs (PWID) demonstrated sustained high positivity rate over the 5 years. About 80% of those initiated on treatment were female sex workers (FSW) and men who have sex with men (MSM) contributing to 41.8% and 38.5% respectively. Year on year, the number of AP receiving ART more than doubled in 2020 and grew by 85%, 43% and 30% in 2021, 2022 and 2023 respectively. There was progressive increase in VL testing coverage between Year 1 – Year 3 across all the three AP typologies and then steady decline between Year 4 – Year 5. Between Year 1 – Year 2 the viral load suppression was at 91% with remarkable improvement to 97% in Year 3 and Year 4 and at 99% in Year 5.ConclusionThe implementation of people-centered, evidence-driven, culturally, and religiously sensitive program enabled SFH to reach a high number of AP in northern Nigeria. This helps improve equity in access to care by AP. There are specific program areas that need continuous improvement including strategies to reach MSM to avoid the evolution of new structural barriers; expansion of PWID programming to optimize all aspects of harm reduction; and sustained sensitization, education, and awareness creation among AP to improve uptake of PrEP and other prevention and care services.

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Conrad Hackett; Marcin Stonawski; Yunping Tong; Stephanie Kramer; Anne Fengyan Shi (2025). Dataset of Global Religious Composition Estimates for 2010 and 2020 [Dataset]. http://doi.org/10.58094/vhrw-k516
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Dataset of Global Religious Composition Estimates for 2010 and 2020

Explore at:
Dataset updated
2025
Dataset provided by
Pew Research Centerhttp://pewresearch.org/
datacite
Authors
Conrad Hackett; Marcin Stonawski; Yunping Tong; Stephanie Kramer; Anne Fengyan Shi
License

https://www.pewresearch.org/about/terms-and-conditions/https://www.pewresearch.org/about/terms-and-conditions/

Dataset funded by
Pew Charitable Trusts
John Templeton Foundation
Description

This dataset describes the world’s religious makeup in 2020 and 2010. We focus on seven categories: Christians, Muslims, Hindus, Buddhists, Jews, people who belong to other religions, and those who are religiously unaffiliated. This analysis is based on more than 2,700 sources of data, including national censuses, large-scale demographic surveys, general population surveys and population registers. For more information about this data, see the associated Pew Research Center report "How the Global Religious Landscape Changed From 2010 to 2020."

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