International Institute for Population Sciences (IIPS) and ICF
24
Ministry of Health and Family Welfare
17
Ministry of Health, Rwanda
17
The DHS Program, Rockville
17
MEASURE DHS
15
UNFPA
15
WHO
13
World Health Organisation (WHO)
13
National Population Commission (NPC), Abuja
11
PEPFAR
10
United Nations Children's Fund (UNICEF)
10
International Food Policy Research Institute
9
Ministry of Health, Malawi
9
ICF
8
Ministry of Health, Nepal
8
National Institute of Statistics of Rwanda (NISR) [Rwanda], Ministry of Health (MOH) [Rwanda], and ICF International
8
The Republic of the Union of Myanmar
8
AIDSinfo
7
DHS
7
New Era
7
DHS Program
6
DHS Program Demographic and Health Surveys
6
Federal Ministry of Health, Nigeria
6
Government of Sierra Leone, Ministry of Health and Sanitation
6
Ministry of Health and Social Services (MOHSS)
6
Ministry of Health, Ghana
6
PLOS One
6
The DHS Program ICF
6
USAID, SPRING
6
and ICF
5
ICF International, et al.
5
Ministry of health
5
Ministry of Health (MoH), Lesotho and ICF International
5
Ministry of Health, Federal Democratic Republic of Ethiopia
5
Ministry of Health, Zambia
5
Nepal Ministry of Health and Population, New ERA, and ICF International
5
The DHS Program ICF International
5
The Republic of the Union of Myanmar, Ministry of Health and Sports
5
United Nations Children’s Fund (UNICEF)
5
United States Agency for International Development
5
Agence Nationale de la Statistique et de la Démographie (ANSD) Dakar, Sénégal et MEASURE DHS, ICF International
4
Cassels, Andrew
4
Central Statistical Office Lusaka
4
Countdown to 2015
4
Deutsche Hauptstelle für Suchtfragen e.V.
4
Direction de la Démographie et des Statistiques Sociales (DDSS) de l’Institut National de la Statistique (INSTAT), ICF International
4
Institut National de la Statistique (INSTAT), Programme National de lutte contre le Paludisme (PNLP), Institut Pasteur de Madagascar (IPM) et ICF International
4
Ministère du Plan et Suivi de la Mise en œuvre de la Révolution de la Modernité (MPSMRM), Ministère de la Santé Publique (MSP) and ICF International
4
Ministry of Health & Social Welfare, Liberia
4
Ministry of Health Zambia
4
Ministry of Health, Rwanda & Rwanda Biomedical Centre
4
Ministry of Health, Uganda
4
National Population Commission (NPC
4
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4
Phone Myint
4
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4
Save the Children
4
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4
Than Tun Sein
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4
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4
World Health Organization (WHO), Regional Office for South-East-Asia
4
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3
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3
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3
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3
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3
CDC
3
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3
EngenderHealth
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3
Ghana AIDS Commission
3
Ghana Statistical Service
3
ICF Macro
3
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3
Kenya National Bureau of Statistics, UNICEF
3
le Ministère de la Santé et de l’Action Sociale (MSAS)
3
L’Agence Nationale de la Statistique et de la Démographie (ANSD), The DHS Program
3
MamaYe Sierra Leone
3
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3
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3
Ministry of Health and Sanitation, Sierra Leone
3
Ministry of Health and Social Services (MoHSS), Namibia
3
Ministry of Health and Social Welfare, Liberia
3
Ministry of Health and Social Welfare, Tanzania
3
Ministry of Health Bangladesh
3
Ministry of Health, Kenya
3
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3
National Institute of Statistics of Rwanda (NISR) [Rwanda], Ministry of Health (MOH) [Rwanda], and ICF International.
3
New ERA, Nepal
3
Organisation Mondiale de la Santé OMS
3
Pan American Health Organization
3
Pan American Health Organization (PAHO)
3
President's Emergency Plan for AIDS Relief (PEPFAR)
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PRESIDENT’S MALARIA INITIATIVE
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Rockville, Maryland, USA: ICF
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The DHS Program, et al.
3
The Government of Malawi
3
The World Bank
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Track20
3
Uganda Aids Commission
3
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3
USAID (From the American People)
3
World Bank
3
World Health Organization (Americas)
3
World Health Organization WHO, Regional Office for Africa
3
World Health Organization, Regional Office for Africa
3
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2
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Agence Nationale de la Statistique et de la Démographie (ANSD)
2
Agence Nationale de la Statistique et de la Démographie (ANSD) [Sénégal] et ICF
2
Agence Nationale de la Statistique et de la Démographie (ANSD) [Sénégal], et ICF
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Assessment Capacities Project
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BASICS
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Brunner B., J. Barnes, A. Carmona et. al.
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Central Bureau of Statistics, National Planning Commission
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Central Statistical Office (CSO) & MEASURE DHS
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Centro Paraguayo de Estudios de Población
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Countdown to 2030
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D. Richardson
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Demography and Social Statistics Division National Statistical Office, The DHS Program
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Federal Ministry of Health, Ethiopia
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
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2
National Institute of Statistics of Rwanda (NISR) & MEASURE DHS - ICF International
2
National Population Commission (NPC Nigeria) and ICF International
2
Organisation Mondiale de la Santé OMS, et al.
2
Rwanda Biomedical Center - Institute of HIV
2
S. Gwavuya
2
Samwel Mbugua, Kerry L. D. MacQuarrie
2
Statistics Sierra Leone
2
TAJSTAN
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The Center for Disease Dynamics, Economics & Policy CDDEP
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The Child Protection Initiative, Save the Children
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The DHS Programm
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The Republic of the Union of Myanmar, Ministry of Health, Department of Health, Child Health Division
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World Health Organization WHO, Regional Office of Africa
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and ICF.
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and S. Ayenalam Kidanie
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1
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1
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1
Wang, W., Do, M., Hembling, J. & Ametepi, P.
1
Wang, Wenjuan, and Rathavuth Hong
1
Wang, Wenjuan, Gheda Temsah, and Emily Carter
1
Wang, Wenjuan, Michelle Winner, Clara Burgert, and Josh Colston
1
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1
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1
water and sanitation program (wsp)
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РЕСПУБЛИКА ТАДЖИКИСТАН
1
Российский государственный медицинский университет им. Н.И. Пирогова Кафедра неврологии и нейрохирургии лечебного факультета д.м.н., доцент Е.А. Катунина, к.м.н. Ю.Н. Бездольный
This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. In addition to visual inspection of laboratory results, we examined how accounting for potential misclassification of HIV status through Bayesian latent
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class models affected the prevalence estimates. Two types of Bayesian models were specified: a model that only uses the individual dichotomous test results and a continuous model that uses the quantitative information of the EIA (i.e., the signal-to-cutoff values). Overall, we found that adjusted prevalence estimates matched the surveys’ original results, with overlapping uncertainty intervals. This suggested that misclassification of HIV status should not affect the prevalence estimates in most surveys. However, our analyses suggested that two surveys may be problematic. The prevalence could have been overestimated in the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, although the magnitude of overestimation remains difficult to ascertain. Interpreting results from the Uganda survey is difficult because of the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite the limitations of our latent class models, our analyses suggest that prevalence estimates from most of the surveys reviewed are not affected by sample misclassification.
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The DHS report itself explains the purpose was, “to obtain and provide information on basic indicators of social progress including fertility, childhood mortality, reproductive and child health, nutritional status of children, and awareness of HIV
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/AIDS and other health-related issues” in PNG. This is important because a DHS then provides the evidence base for PNG officials themselves to track progress in PNG over time, compare trends with other comparable countries, and then allocate financial and human resources to where they are needed most.
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Massoda Tonye et al. Malar J (2018) 17:156
https://doi.org/10.1186/s12936-018-2284-7
Background: In 2011, the demographic and health survey (DHS) in Cameroon was combined with the multiple indicator
cluster survey. Malaria parasitological data
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were collected, but the survey period did not overlap with the high
malaria transmission season. A malaria indicator survey (MIS) was also conducted during the same year, within the
malaria peak transmission season. This study compares estimates of the geographical distribution of malaria parasite
risk and of the effects of interventions obtained from the DHS and MIS survey data.
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This study is a theory-driven analysis of the socio-demographic determinants of maternal care seeking in Kenya. Specifically, it examines predisposing, enabling, and need factors potentially associated with use of antenatal care (ANC), health facility delivery, and timely postnatal care (PNC).This s
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tudy uses data from the 2014 Kenya Demographic and Health Survey (KDHS) conducted among women age 15-49 with a live birth in the five years preceding the survey. It includes data from all 47 counties of Kenya, grouped contiguously into 12regions.We apply Andersen’s Behavioral Model of Health Services Use to examine socio-demographic predictors of health service use.We estimate logistic regression models for adequate use of ANC (defined as attending at least four ANC visits, starting in the first three months of pregnancy), delivery in a health facility, and PNC within 48 hours of delivery.
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Barriers to the prompt and effective diagnosis and treatment of malaria exist at both the community and health facility level. Household surveys measure malaria case management at the population level with standard indicators that assess treatment-seeking behavior, access to diagnostic testing, and
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access to appropriate treatment. Performance on these indicators varies widely from country to country. Among countries with Demographic and Health Surveys (DHS) or Malaria Indicator Surveys (MIS) completed between 2014 and 2016, advice and treatment was sought for a median of 47% of children under age 5 with fever.
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