This global accessibility map enumerates land-based travel time to the nearest densely-populated area for all areas between 85 degrees north and 60 degrees south for a nominal year 2015. Densely-populated areas are defined as contiguous areas with 1,500 or more inhabitants per square kilometre or a majority of built-up land cover types coincident with a population centre of at least 50,000 inhabitants. This map was produced through a collaboration between MAP (University of Oxford), Google, the European Union Joint Research Centre (JRC), and the University of Twente, Netherlands.The underlying datasets used to produce the map include roads (comprising the first ever global-scale use of Open Street Map and Google roads datasets), railways, rivers, lakes, oceans, topographic conditions (slope and elevation), landcover types, and national borders. These datasets were each allocated a speed or speeds of travel in terms of time to cross each pixel of that type. The datasets were then combined to produce a "friction surface"; a map where every pixel is allocated a nominal overall speed of travel based on the types occurring within that pixel. Least-cost-path algorithms (running in Google Earth Engine and, for high-latitude areas, in R) were used in conjunction with this friction surface to calculate the time of travel from all locations to the nearest (in time) city. The cities dataset used is the high-density-cover product created by the Global Human Settlement Project. Each pixel in the resultant accessibility map thus represents the modelled shortest time from that location to a city. Authors: D.J. Weiss, A. Nelson, H.S. Gibson, W. Temperley, S. Peedell, A. Lieber, M. Hancher, E. Poyart, S. Belchior, N. Fullman, B. Mappin, U. Dalrymple, J. Rozier, T.C.D. Lucas, R.E. Howes, L.S. Tusting, S.Y. Kang, E. Cameron, D. Bisanzio, K.E. Battle, S. Bhatt, and P.W. Gething. A global map of travel time to cities to assess inequalities in accessibility in 2015. (2018). Nature. doi:10.1038/nature25181

Processing notes: Data were processed from numerous sources including OpenStreetMap, Google Maps, Land Cover mapping, and others, to generate a global friction surface of average land-based travel speed. This accessibility surface was then derived from that friction surface via a least-cost-path algorithm finding at each location the closest point from global databases of population centres and densely-populated areas. Please see the associated publication for full details of the processing.

Source: https://map.ox.ac.uk/research-project/accessibility_to_cities/

BackgroundInfection by the simian malaria parasite, Plasmodium knowlesi, can lead to severe and fatal disease in humans, and is the most common cause of malaria in parts of Malaysia. Despite being a serious public health concern, the geographical distribution of P. knowlesi malaria risk is poorly understood because the parasite is often misidentified as one of the human malarias. Human cases have been confirmed in at least nine Southeast Asian countries, many of which are making progress towards eliminating the human malarias. Understanding the geographical distribution of P. knowlesi is important for identifying areas where malaria transmission will continue after the human malarias have been eliminated.Methodology/Principal FindingsA total of 439 records of P. knowlesi infections in humans, macaque reservoir and vector species were collated. To predict spatial variation in disease risk, a model was fitted using records from countries where the infection data coverage is high. Predictions were then made throughout Southeast Asia, including regions where infection data are sparse. The resulting map predicts areas of high risk for P. knowlesi infection in a number of countries that are forecast to be malaria-free by 2025 (Malaysia, Cambodia, Thailand and Vietnam) as well as countries projected to be eliminating malaria (Myanmar, Laos, Indonesia and the Philippines).Conclusions/SignificanceWe have produced the first map of P. knowlesi malaria risk, at a fine-scale resolution, to identify priority areas for surveillance based on regions with sparse data and high estimated risk. Our map provides an initial evidence base to better understand the spatial distribution of this disease and its potential wider contribution to malaria incidence. Considering malaria elimination goals, areas for prioritised surveillance are identified.

Users can view maps, spatial, and statistical information drawn from different databases around the world. In addition, users can download data sets pertaining to prevalence and location of health facilities. Background The World Health Organization GeoNetwork is a geographic information management system that contains geo-referenced data sets and maps to facilitate the planning and monitoring of health related activities and health conditions. Information is available regarding the prevalence and location of health facilities. User Functionality Users must download the Geographic Information Systems (GIS) and Remote-Sensing (RSS) software applications to interact with the data tools, including digital maps, satellite images, and other geographic information. To obtain maps and other geographic information, users can search by term or geographic location or conduct an advanced search by time frame, year, and geographic location. There is a useful manual located under the “Help” tab, which enables users to learn more about GIS and how to use the GeoNetwork. Data Notes Data sources include: Food and Agriculture Organization of the United Nations (FAO), World Food Programme (WFP), and the United Nations Environment Programme (UNEP). The website announces datasets that have most recently been added to the GeoNetwork, but does not indicate the date it was updated.

This layer contains the data of state level India Malaria (2015-2020) and contains information about Malaria cases in 2015, Malaria cases in 2016, Malaria deaths in 2016, Malaria cases in 2017 etc.About MalariaMalaria is a potentially life-threatening disease caused by parasites (Plasmodium vivax, Plasmodium falciparum, Plasmodium malaria and Plasmodium ovale) that are transmitted through the bite of infected female Anopheles mosquitoes.Symptoms of Malaria It includes fever and flu-like illness, including shaking chills, headache, muscle aches, and tiredness. Nausea, vomiting, and diarrhea may also occur. Malaria may cause anemia and jaundice (yellow coloring of the skin and eyes) because of the loss of red blood cells. If not promptly treated, the infection can become severe and may cause kidney failure, seizures, mental confusion, coma, and death.Malaria in IndiaAccording to the World malaria report 2019, India represents 3% of the global malaria burden. Despite being the highest malaria burden country of the SEA region, India showed a reduction in reported malaria cases of 49% and deaths of 50.5% compared with 2017.India has a vision of a malaria free country by 2027 and elimination by 2030.The attributes are given below for this web map:Malaria Cases in 2015Malaria Cases in 2016Malaria Deaths in 2016Malaria Cases in 2017Malaria Deaths in 2017Malaria Cases in 2018Malaria Deaths in 2018Malaria Cases in 2019Malaria Deaths in 2019Malaria Cases in 2020Malaria Deaths in 2020This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

BackgroundComprehensive and contemporary estimates of the number of pregnancies at risk of malaria are not currently available, particularly for endemic areas outside of Africa. We derived global estimates of the number of women who became pregnant in 2007 in areas with Plasmodium falciparum and P. vivax transmission.Methods and FindingsA recently published map of the global limits of P. falciparum transmission and an updated map of the limits of P. vivax transmission were combined with gridded population data and growth rates to estimate total populations at risk of malaria in 2007. Country-specific demographic data from the United Nations on age, sex, and total fertility rates were used to estimate the number of women of child-bearing age and the annual rate of live births. Subregional estimates of the number of induced abortions and country-specific stillbirths rates were obtained from recently published reviews. The number of miscarriages was estimated from the number of live births and corrected for induced abortion rates. The number of clinically recognised pregnancies at risk was then calculated as the sum of the number of live births, induced abortions, spontaneous miscarriages, and stillbirths among the population at risk in 2007. In 2007, 125.2 million pregnancies occurred in areas with P. falciparum and/or P. vivax transmission resulting in 82.6 million live births. This included 77.4, 30.3, 13.1, and 4.3 million pregnancies in the countries falling under the World Health Organization (WHO) regional offices for South-East-Asia (SEARO) and the Western-Pacific (WPRO) combined, Africa (AFRO), Europe and the Eastern Mediterranean (EURO/EMRO), and the Americas (AMRO), respectively. Of 85.3 million pregnancies in areas with P. falciparum transmission, 54.7 million occurred in areas with stable transmission and 30.6 million in areas with unstable transmission (clinical incidence

PSI/Liberia was founded in 2008 to work in partnership with the Ministry of Health and Social Welfare to implement the Basic Package of Health Service component of the National Health Plan. Specifically, this MAP study aimed at assessing the availability of condoms, WaterGuard and other household water treatment products, oral and injectable contraceptives and malaria treatment drugs throughout the country. This information can be vital for the sales and marketing teams in prioritizing their efforts, both for the two existing PSI products and as a baseline for the distribution of two potential new health products (ACTs and oral contraceptives). Up to now, no such availability studies have been conducted in Liberia; results are thus expected to be of use to a range of key stakeholders in the public health sector of the country, particularly to the Ministry of Health and Social Welfare, the National AIDS and STI Control Program, the National Malaria Control Program, and public health NGOs.] [The MAP methodology employs the Lot Quality Assurance Sampling (LQAS) technique to draw a random sample of 19 enumeration areas (EAs) from each of five supervision areas (Greater Monrovia, South East, South Central, North West, North Central). This LQAS assessment of coverage determines the proportion of enumeration areas in which a product is available in each of these regions. Very low populated and inaccessible areas were excluded from the study. All outlets in the selected EAs were audited, including health facilities, pharmacies/drug stores, private clinics, medicine vendors, small shops (kiosks, business centers), street vendors, etc. For condoms, entertainment venues such as bars, night clubs and hotels were also visited. Additional information was collected on the source of supply of PSI products, reasons for not selling a particular product, interest in selling condoms/WaterGuard the products for outlets which never stocked the product. Data was collected in May 2011.

The dates of detection of index cases are displayed. The red arrows show the spread of antimalarial resistance from East Africa to West Africa.

Abstract

The primary objective of the 2022 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2022 TDHSMIS) is to provide current and reliable information on population and health issues. Specifically, the 2022 TDHS-MIS collected information on marriage and sexual activity, fertility and fertility preferences, family planning, infant and child mortality, maternal health care, disability among the household population, child health, nutrition of children and women, malaria prevalence, knowledge, and communication, women’s empowerment, women’s experience of domestic violence, adult maternal mortality via sisterhood method, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), female genital cutting, and early childhood development. Other information collected on health-related issues included smoking, blood pressure, anaemia, malaria, and iodine testing, height and weight, and micronutrients.

The information collected through the 2022 TDHS-MIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of Tanzania’s population. The 2022 TDHS-MIS also provides indicators to monitor and evaluate international, regional, and national programmes, such as the Global Agenda 2030 on Sustainable Development Goals (2030 SDGs), Tanzania Development Vision 2025, the Third National Five-Year Development Plan (FYDP III 2021/22–2025/26), East Africa Community Vision 2050 (EAC 2050), and Africa Development Agenda 2063 (ADA 2063).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample design for the 2022 TDHS-MIS was carried out in two stages and was intended to provide estimates for the entire country, for urban and rural areas in Tanzania Mainland, and for Zanzibar. For specific indicators such as contraceptive use, the sample design allows for estimation of indicators for each of the 31 regions—26 regions in Tanzania Mainland and 5 regions in Zanzibar.

The sampling frame excluded institutional populations, such as persons in hospitals, hotels, barracks, camps, hostels, and prisons. The 2022 TDHS-MIS followed a stratified two-stage sample design. The first stage involved selection of sampling points (clusters) consisting of enumeration areas (EAs) delineated for the 2012 Tanzania Population and Housing Census (2012 PHC). The EAs were selected with a probability proportional to their size within each sampling stratum. A total of 629 clusters were selected. Among the 629 EAs, 211 were from urban areas and 418 were from rural areas.

In the second stage, 26 households were selected systematically from each cluster, for a total anticipated sample size of 16,354 households for the 2022 TDHS-MIS. A household listing operation was carried out in all the selected EAs before the main survey. During the household listing operation, field staff visited each of the selected EAs to draw location maps and detailed sketch maps and to list all residential households found in each EA with addresses and the names of the heads of the households. The resulting list of households served as a sampling frame for the selection of households in the second stage. During the listing operation, field teams collected global positioning system (GPS) data—latitude, longitude, and altitude readings—to produce one GPS point per EA. To estimate geographic differentials for certain demographic indicators, Tanzania was divided into nine geographic zones. Although these zones are not official administrative areas, this classification system is also used by the Reproductive and Child Health Section of the Ministry of Health. Grouping of regions into zones allows for larger denominators and smaller sampling errors for indicators at the zonal level.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2022 TDHS-MIS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Micronutrient Questionnaire. The questionnaires, based on The DHS Program’s Model Questionnaires, were adapted to reflect the population and health issues relevant to Tanzania. In addition, a self-administered Fieldworker’s Questionnaire collected information about the survey’s fieldworkers.

Cleaning operations

In the 2022 TDHS-MIS survey, CAPI was used during data collection. The devices used for CAPI were Android-based computer tablets programmed using a mobile version of CSPro. Programming of questionnaires into the android application was done by ICF, while configuration of tablets was done by NBS and OCGS in collaboration with ICF. All fieldwork personnel were assigned usernames, and devices were password protected to ensure the integrity of the data collected. Selected households were assigned to CAPI supervisors, whereas households were assigned to interviewers’ tablets via Bluetooth. The data for all interviewed households were sent back to CAPI supervisors, who were responsible for initial data consistency and editing, before being sent to the central servers hosted at NBS Headquarters via Syncloud.

The data processing of the 2022 TDHS-MIS ran concurrently with the data collection exercise. The electronic data files from each completed cluster were transferred via Syncloud to the NBS central office server in Dodoma. The data files were registered and checked for inconsistencies, incompleteness, and outliers. Errors and inconsistencies were communicated to the field teams for review and correction. Secondary central data editing was done by NBS and OCGS survey staff at the central office. A CSPro batch editing tool was used for cleaning data and included coding of open-ended questions and resolving inconsistencies.

The Biomarker paper questionnaires were collected by field supervisors and compared with the electronic data files to check for any inconsistencies that may have occurred during data entry. The concurrent data collection and processing offered an advantage because it maximised the likelihood of having error-free data. Timely generation of field check tables allowed effective monitoring. The secondary data editing exercise was completed in October 2022.

Response rate

A total of 16,312 households were selected for the 2022 TDHS-MIS sample. This number is slightly less than the targeted sample size of 16,354 because one EA could not be reached due to security reasons, while a few EAs had less than the targeted 26 households. Of the 16,312 households selected, 15,907 were found to be occupied. Of the occupied households, 15,705 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,699 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,254 women, yielding a response rate of 97%. In the subsample (50% of households) of households selected for the male questionnaire, 6,367 men age 15–49 were identified as eligible for individual interviews, and 5,763 were successfully interviewed, yielding a response rate of 91%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Tanzania Demographic and Health Survey and Malaria Indicator Survey (2022 TDHS-MIS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 TDHS-MIS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 TDHS-MIS sample was the result of a multistage stratified design, and,

Abstract

The principal objective of the 2009-10 Timor-Leste Demographic and Health Survey (TLDHS) was to provide current and reliable data on fertility and family planning behavior, child mortality, adult and maternal mortality, child nutritional status, the utilization of maternal and child health services, and knowledge of HIV/AIDS.

The specific objectives of the survey were to: - collect data at the national level that will allow the calculation of key demographic rates; - analyze the direct and indirect factors that determine the levels and trends in fertility; - measure the level of contraceptive knowledge among women and men, and measure the level of practice among women by method, according to urban or rural residence; - collect quality data on family health, including immunization coverage among children, prevalence and treatment of diarrhea and other diseases among children under age 5, and maternity care indicators, including antenatal visits, assistance at delivery, and postnatal care; - collect data on infant and child mortality and on maternal and adult mortality; - obtain data on child feeding practices, including breastfeeding, and collect anthropometric measures to use in assessing the nutritional status of women and children; - collect information on knowledge of tuberculosis (TB), knowledge of the spread of TB, and attitudes towards people infected with TB among women and men; - collect data on use of treated and untreated mosquito nets, persons who sleep under the nets, use of drugs for malaria during pregnancy, and use of antimalarial drugs fortreatment of fever among children under age 5; - collect data on knowledge and attitudes of women and men about sexually transmitted infections and HIV/AIDS, and evaluate patterns of recent behavior regarding condom use; - collect information on the sexual practices of women and men; their number of sexual partners in the past 12 months, and over their lifetime; risky sexual behavior, including condom use at last sexual intercourse; and payment for sex; - conduct hemoglobin testing on women age 15-49 and children age 6-59 months in a subsample of households selected for the survey to provide information on the prevalence of anemia among women of reproductive age and young children; - collect information on domestic violence

This information is essential for informed policy decisions, planning, monitoring, and evaluation of programs on health in general, and on reproductive health in particular, at both the national and district levels. A long-term objective of the survey is to strengthen the technical capacity of government organizations to plan, conduct, process, and analyze data from complex national population and health surveys. Moreover, the 2009-10 TLDHS provides national and district-level estimates on population and health that are comparable to data collected in similar surveys in other developing countries. The first Demographic and Health Survey (DHS) in Timor-Leste was done in 2003. Unlike the 2003 DHS, however, the 2009-10 TLDHS was conducted under the worldwide MEASURE DHS program, funded by the United States Agency for International Development (USAID) and with technical assistance provided by ICF Macro. Data from the 2009-10 TLDHS allow for comparison of information gathered over a longer period of time and add to the vast and growing international database on demographic and health variables.

The 2009-10 TLDHS supplements and complements the information collected through the censuses, updates the available information on population and health issues, and provides guidance in planning, implementing, monitoring and evaluating Timor-Leste's health programs. Further, the results of the survey assist in monitoring the progress made towards meeting the Millennium Development Goals (MDGs) and other international initiatives.

The 2009-10 TLDHS includes topics related to fertility levels and determinants; family planning; fertility preferences; infant, child, adult and maternal mortality; maternal and child health; nutrition; malaria; domestic violence; knowledge of HIV/AIDS and women's empowerment. The 2009-10 TLDHS for the first time also includes anemia testing among women age 15-49 and children age 6-59 months. As well as providing national estimates, the survey also provides disaggregated data at the level of various domains such as administrative district, as well as for urban and rural areas. This being the third survey of its kind in the country (after the 2002 MICS and the 2003 DHS), there is considerable trend information on demographic and reproductive health indicators.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary focus of the 2009-10 TLDHS was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of most key variables for the 13 districts.

Sampling Frame

The TLDHS used the sampling frame provided by the list of census enumeration areas (EAs) with population and household information from the 2004 Population and Housing Census (PHC). Administratively, Timor-Leste is divided into 13 districts. Stratification is achieved by separating each of the 13 districts into urban and rural areas. In total, 26 sampling strata were created. Samples were selected independently in every stratum, through a two-stage selection process. Implicit stratification was achieved at each of the lower administrative levels by sorting the sampling frame before sample selection, both according to administrative units and also by using a probability proportional-to-size selection at the first stage of sampling. The implicit stratification also allowed for the proportional allocation of sample points at each of the lower administrative levels.

Sample Selection

At the first stage of sampling, 455 enumeration areas (116 urban areas and 339 rural areas) were selected with probability proportional to the EA size, which is the number of households residing in the EA at the time of the census. A complete household listing operation in all of the selected EAs is the usual procedure to provide a sampling frame for the second-stage selection of households. However, a complete household listing was only carried out in select clusters in Dili, Ermera, and Viqueque, where more than 20 percent of the households had been destroyed. In all other clusters, a complete household listing was not possible because the country does not have written boundary maps for clusters. Instead, using the GPS coordinate locations for structures in each selected cluster as provided for by the 2004 PHC, households were randomly selected using their Geographic Information System (GIS) location identification in the central office. A map for each cluster was then generated, marking the households to be surveyed with their location identification. The maps also contained all the other households, roads, rivers, and major landmarks for easier location of selected households in the field. To provide statistically reliable estimates of key demographic and health variables and to cater for nonresponse, 27 households each were selected.

The survey was designed to cover a nationally representative sample of 12,285 residential households, taking into account nonresponse; to obtain completed interviews of 11,800 women age 15-49 in every selected household; and to obtain completed interviews of 3,800 men age 15-49 in every third selected household.

Note: See detailed description of the sample design in Appendix A of the report presented in this documentation.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were administered in the TLDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. These questionnaires were adapted from the standard MEASURE DHS core questionnaires to reflect the population and health issues relevant to Timor-Leste based on a series of meetings with various stakeholders from government ministries and agencies, NGOs, and international donors. The final draft of each questionnaire was discussed at a questionnaire design workshop organized by NSD on March 10, 2009, in Dili. These questionnaires were then translated and back translated from English into the two main local languages-Tetum and Bahasa—and pretested prior to the main fieldwork to ensure that the original meanings of the questions were not lost in translation.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. For children under age 18, survival status of the parents was determined. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, ownership of various durable goods, and ownership of mosquito nets. Additionally, the Household Questionnaire was used to record height and weight measurements for women age 15-49 and children under age 5, and to list hemoglobin measurements for women age 15-49 and children age 6-59 months.

The Woman’s Questionnaire was used to collect information from women age 15-49.