United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data was reported at 0.500 % in 2014. This stayed constant from the previous number of 0.500 % for 2013. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data is updated yearly, averaging 0.500 % from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 0.500 % in 2014 and a record low of 0.500 % in 2014. United States US: Prevalence of HIV: Total: % of Population Aged 15-49 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of HIV refers to the percentage of people ages 15-49 who are infected with HIV.; ; UNAIDS estimates.; Weighted Average;

This directory is for at-risk for HIV and eligible persons living with HIV in New York City seeking HIV medical and supportive services. The agencies and their listed programs receive CDC and Ryan White Part-A funding to provide: Targeted-Testing among Priority Populations, Food and Nutrition Services, Health Education and Risk Reduction Services, Harm Reduction Services, Legal Services, Mental Health Services, Case Management and Care Coordination Services, and Supportive Counseling Services. To be eligible to recieve these services, prospective clients must: 1)be HIV-positive; 2) have a total household income below 435% of the Federal Poverty Level (FPL) (this is the same as the income eligible guidelines for the New York State AIDS Drug Assistance Program (ADAP) and higher than the income eligiblity guidelines for Medicaid in New York State); and 3) reside in New York City or the counties of Westchester, Rockland, and Putnam. For providers, to make a referral, please contact the program directly using the information provided in the diretory (please be sure to call before directing clients to the program). When making a referral, you may also find it useful to talk to your client about executing a release of information form authorizing you to share confidential health and HIV-related information with another service provider in order to coordinate care (for more information, go to https://www.health.ny.gov/diseases/aids/providers/forms/informedconsent.htm).

Abstract

The 2018 Nigeria AIDS Indicator and Impact Survey (NAIIS) is a cross-sectional survey that will assess the prevalence of key human immunodeficiency virus (HIV)-related health indicators. This survey is a two-stage cluster survey of 88,775 randomly-selected households in Nigeria, sampled from among 3,551 nationally-representative sample clusters. The survey is expected to include approximately 168,029 participants, ages 15-64 years and children, ages 0-14 years, from the selected household. The 2018 NAIIS will characterize HIV incidence, prevalence, viral load suppression, CD4 T-cell distribution, and risk behaviors in a household-based, nationally-representative sample of the population of Nigeria, and will describe uptake of key HIV prevention, care, and treatment services. The 2018 NAIIS will also estimate the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV) infections, and HBV/HIV and HCV/HIV co-infections.

Geographic coverage

National coverage, the survey covered the Federal Republic and was undertaken in each state and the Federal Capital.

Analysis unit

Household Health Survey

Universe

1. Women and men aged 15-64 years living in residential households and visitors who slept in the household the night before the survey
2. Children aged 0-14 years living in residential households and child visitors who slept in the household the night before the survey

Kind of data

Sample survey data [ssd]

Sampling procedure

This cross-sectional, household-based survey uses a two-stage cluster sampling design (enumeration area followed by households). The target population is people 15-64 and children ages 0-14 years. The overall size and distribution of the sample is determined by analysis of existing estimates of national HIV incidence, sub-national HIV prevalence, and the number of HIV-positive cases needed to obtain estimates of VLS among adults 15-64 years for each of the 36 states and the FCT while not unnecessarily inflating the sample size needed.

From a sampling perspective, the three primary objectives of this proposal are based on competing demands, one focused on national incidence and the other on state-level estimates in a large number of states (37). Since the denominator used for estimating VLS is HIV-positive individuals, the required minimum number of blood draws in a stratum is inversely proportional to the expected HIV prevalence rate in that stratum. This objective requires a disproportionate amount of sample to be allocated to states with the lowest prevalence. A review of state-level prevalence estimates for sources in the last 3 to 5 years shows that state-level estimates are often divergent from one source to the next, making it difficult to ascertain the sample size needed to obtain the roughly 100 PLHIV needed to achieve a 95% confidence interval (CI) of +/- 10 for VLS estimates.

An equal-size approach is proposed with a sample size of 3,700 blood specimens in each state. Three-thousand seven hundred specimens will be sufficiently large to obtain robust estimates of HIV prevalence and VLS among HIV-infected individuals in most states. In states with a HIV prevalence above 2.5%, we can anticipate 95% CI of less than +/-10% and relative standard errors (RSEs) of less than 11% for estimates of VLS. In these states, with HIV prevalence above 2.5%, the anticipated 95% CI around prevalence is +/- 0.7% to a high of 1.1-1.3% in states with prevalence above 6%. In states with prevalence between 1.2 and 2.5% HIV prevalence estimates would remain robust with 95% CI of +/- 0.5-0.6% and RSE of less than 20% while 95% CI around VLS would range between 10-15% (and RSE below 15%). With this proposal only a few states, with HIV prevalence below 1.0%, would have less than robust estimates for VLS and HIV prevalence.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2018 NAIIS: Household Questionnaire, Adult Questionnaire, and Early Adolescent Questionnaire (10-14 Years).

Cleaning operations

During the household data collection, questionnaire and laboratory data were transmitted between tablets via Bluetooth connection. This facilitated synchronization of household rosters and ensured data collection for each participant followed the correct pathway. All field data collected in CSPro and the Laboratory Data Management System (LDMS) were transmitted to a central server using File Transfer Protocol Secure (FTPS) over a 4G or 3G telecommunication provider at least once a day. Questionnaire data cleaning was conducted using CSPro and SAS 9.4 (SAS Institute Inc., Cary, North Carolina, United States). Laboratory data were cleaned and merged with the final questionnaire database using unique specimen barcodes and study identification numbers.

Response rate

A total of 101,267 households were selected, 89,345 were occupied and 83,909 completed the household interview . • For adults aged 15-64 years, interview response rate was 91.6% for women and 88.2% for men; blood draw response rate was 92.9% for women and 93.6% for men. • For adolescents aged 10-14 years, interview response rate was 86.8% for women and 86.2% for men; blood draw response rate was 91.2% for women and 92.3% for men. • For children aged 0-9 years, blood draw response rate was 68.5% for women and men.

Sampling error estimates

Estimates from sample surveys are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors result from mistakes made during data collection, e.g., misinterpretation of an HIV test result and data management errors such as transcription errors during data entry. While NAIIS implemented numerous quality assurance and control measures to minimize non-sampling errors, these were impossible to avoid and difficult to evaluate statistically. In contrast, sampling errors can be evaluated statistically. Sampling errors are a measure of the variability between all possible samples.

The sample of respondents selected for NAIIS was only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples could yield results that differed somewhat from the results of the actual sample selected. Although the degree of variability cannot be known exactly, it can be estimated from the survey results. The standard error, which is the square root of the variance, is the usual measurement of sampling error for a statistic (e.g., proportion, mean, rate, count). In turn, 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 approximately plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

NAIIS utilized a multi-stage stratified sample design, which required complex calculations to obtain sampling errors. The Taylor linearization method of variance estimation was used for survey estimates that are proportions, e.g., HIV prevalence. The Jackknife repeated replication method was used for variance estimation of more complex statistics such as rates, e.g., annual HIV incidence and counts such as the number of people living with HIV.

The Taylor linearization method treats any percentage or average as a ratio estimate, , where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: in which Where represents the stratum, which varies from 1 to H, is the total number of clusters selected in the hth stratum, is the sum of the weighted values of variable y in the ith cluster in the hth stratum, is the sum of the weighted number of cases in the ith cluster in the hth stratum and, f is the overall sampling fraction, which is so small that it is ignored.

In addition to the standard error, the design effect for each estimate is also calculated. The design effect is defined as the ratio of the standard error using the given sample design to the standard error that would result if a simple random sample had been used. A design effect of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. Confidence limits for the estimates, which are calculated as where t(0.975, K) is the 97.5th percentile of a t-distribution with K degrees of freedom, are also computed.

Data appraisal

Remote data quality check was carried out using data editor

AimsTwo behavioral HIV prevention interventions for people who inject drugs (PWID) infected with HIV include the Holistic Health Recovery Program for HIV+ (HHRP+), a comprehensive evidence-based CDC-supported program, and an abbreviated Holistic Health for HIV (3H+) Program, an adapted HHRP+ version in treatment settings. We compared the projected health benefits and cost-effectiveness of both programs, in addition to opioid substitution therapy (OST), to the status quo in the U.S.MethodsA dynamic HIV transmission model calibrated to epidemic data of current US populations was created. Projected outcomes include future HIV incidence, HIV prevalence, and quality-adjusted life years (QALYs) gained under alternative strategies. Total medical costs were estimated to compare the cost-effectiveness of each strategy.ResultsOver 10 years, expanding HHRP+ access to 80% of PWID could avert up to 29,000 HIV infections, or 6% of the projected total, at a cost of $7,777/QALY gained. Alternatively, 3H+ could avert 19,000 infections, but is slightly more cost-effective ($7,707/QALY), and remains so under widely varying effectiveness and cost assumptions. Nearly two-thirds of infections averted with either program are among non-PWIDs, due to reduced sexual transmission from PWID to their partners. Expanding these programs with broader OST coverage could avert up to 74,000 HIV infections over 10 years and reduce HIV prevalence from 16.5% to 14.1%, but is substantially more expensive than HHRP+ or 3H+ alone.ConclusionsBoth behavioral interventions were effective and cost-effective at reducing HIV incidence among both PWID and the general adult population; however, 3H+, the economical HHRP+ version, was slightly more cost-effective than HHRP+.

The Philippines reported about ****** HIV cases, an increase from the previous year. The number of reported HIV cases has gradually increased since 2012, aside from a significant dip in 2020. The state of HIV As the monthly average number of people newly diagnosed with HIV increases, the risk it poses threatens the lives of Filipinos. HIV is a sexually transmitted infection that attacks the body’s immune system, with more males being diagnosed than females. In 2022, the majority of people newly diagnosed with HIV were those between the age of 25 and 34 years, followed by those aged 15 and 24. There is still no cure for HIV and without treatment, it could lead to other severe illnesses such as tuberculosis and cancers such as lymphoma and Kaposi’s sarcoma. However, HIV is now a manageable chronic illness that can be treated with proper medication. What are the leading causes of death in the Philippines? Between January and September 2024, preliminary figures have shown that ischaemic heart disease was the leading cause of death in the Philippines. The prevalence of heart diseases in the nation has been closely attributed to the Filipino diet, which was described as having a high fat, high cholesterol, and high sodium content. In addition, acute respiratory infections and hypertension also registered the highest morbidity rate among leading diseases in the country in 2021.

Description: The adult and youth data of the SABSSM 2002 study cover information from adults and youths 15 years and older on topics ranging from biographical information, media and communication, male circumcision, marital status and marriage practice, partner and partner characteristics, sexual behaviour and practices, voluntary counseling and testing (VCT), sexual orientation, interpersonal communication, practices around widowhood, knowledge and perceptions of HIV and AIDS, stigma, hospitalisation and health status. The data set consists of 643 variables and 9788 cases. Abstract: Background: This is the first in a series of national HIV household surveys conducted in South Africa. The survey was commissioned by the Nelson Mandela Children's Fund and the Nelson Mandela Foundation. The key aims were to determine the HIV prevalence in the general population, identify risk factors that increase vulnerability of South Africans to HIV infections, to identify the contexts within which sexual behaviour occurs and the obstacles to risk reduction and to determine the level of exposure of all sectors of society to current prevention. The Nelson Mandela Children's Fund requested the HSRC to assess the impact of current HIV and AIDS education and awareness programmes designed to slow down the epidemic, including infection rates, stigma, care and support for affected individuals and families. Methodology: Sampling methods: multi-stage cluster stratified sample stratified by province, settlement geography (geotype) and predominant race group in each area. A systematic sample of 15 households was drawn from each of 1 000 census enumeration areas (EAs). In each household, one person was randomly selected in each of four mutually exclusive age groups (2-11 years; 12-14 years; 15-24 years; 25+ years). Field workers administered questionnaires to selected respondents and also collected oral fluid specimens for HIV testing. Results: This study sampled a cross-section of 9 963 South Africans aged two years and older. HIV is a generalised epidemic in South Africa that extends to all age groups, geographic areas and race groups. It showed 11.4 % were HIV positive, 15.6 per cent of them aged between 15 and 49. Women (12.8% HIV positive) were more at risk of infection than men (9.5% HIV positive). Urban informal settlements have the highest incidence of HIV infection (21.3%). Free State showed the highest prevalence (14.9%) with Eastern Cape having the lowest (6.6%). Higher rates of infection (5.6%) are also found in children aged 2-14 and Africans (10.2%). Awareness of HIV status was low. Only 18.9% reported that they were previously tested. Fewer women (3.9%) reported more than one sexual partner as compared to men (13.5%). Condom use at last sex was low among both women (24.7%) and men (30.3%). Knowledge of HIV and AIDS is generally high, with sexual behaviour changes taking root in encouragingly low numbers of sexual partners and high levels of abstinence among the youth. There is still great uncertainty of the relationship between HIV and AIDS and popular myths. South Africans from all walks of life are at risk. In particular, wealthy Africans have the same levels of risk as poorer Africans - whereas in other race groups, poorer people are more vulnerable to infection. Conclusions: The study recommended the expansion of voluntary counselling and testing. Prevention programmes ought to focus on reduction on multiple partners and increased condom use. It further recommended, inter alia, that HIV/AIDS prevention programmes be intensified for people living in informal settlements, campaigns be implemented using mass media to address myths and misconceptions and that information needs in rural communities and poorer households due to lack of access to mass media channels, should be attended to.

Analysis of ‘DOHMH HIV Service Directory’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d40a54d7-0c73-46ab-9805-9aaca7dcfe0b on 26 January 2022.

--- Dataset description provided by original source is as follows ---

This directory is for at-risk for HIV and eligible persons living with HIV in New York City seeking HIV medical and supportive services. The agencies and their listed programs receive CDC and Ryan White Part-A funding to provide: Targeted-Testing among Priority Populations, Food and Nutrition Services, Health Education and Risk Reduction Services, Harm Reduction Services, Legal Services, Mental Health Services, Case Management and Care Coordination Services, and Supportive Counseling Services. To be eligible to recieve these services, prospective clients must: 1)be HIV-positive; 2) have a total household income below 435% of the Federal Poverty Level (FPL) (this is the same as the income eligible guidelines for the New York State AIDS Drug Assistance Program (ADAP) and higher than the income eligiblity guidelines for Medicaid in New York State); and 3) reside in New York City or the counties of Westchester, Rockland, and Putnam. For providers, to make a referral, please contact the program directly using the information provided in the diretory (please be sure to call before directing clients to the program). When making a referral, you may also find it useful to talk to your client about executing a release of information form authorizing you to share confidential health and HIV-related information with another service provider in order to coordinate care (for more information, go to https://www.health.ny.gov/diseases/aids/providers/forms/informedconsent.htm).

--- Original source retains full ownership of the source dataset ---

This is one of the three datasets related to the Prevention Agenda Tracking Indicators state level data posted on this site. Each dataset consists of 58 state-level health tracking indicators and 31 sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Priority areas include Chronic Disease; Health and Safe Environment; Healthy Women, Infants and Children; Mental Health and Substance Abuse; and HIV, STDs, Vaccine Preventable Diseases and Healthcare Associated Infections. The latest data dataset includes the most recent state level data for all indicators. The trend dataset includes the most recent state level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among racial, ethnic, and socioeconomic groups and persons with disabilities. For more information, check out: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/ and https://www.health.ny.gov/PreventionAgendaDashboard, or go to the “About” tab.

Enter your study abstract here This dissertation explores the role that organizational learning processes play in state HIV/AIDS policy development. The puzzle addressed is the large degree of variation in policy output across states that are similar in terms of political or economic structure. Although one can tell individual stories about each country, the overall variation defies the cross-applicability of typical explanations. Where states better draw lessons from experience we s hould expect two re- sults. First, structural characteristics of the state or of the set of HIV policy responders affects the character and degree of learning: the configuration of decision-making authority and information analytics interact with the learning process, affecting the lessons drawn and policies pursued. Second, over time we observe some degree of policy convergence among states due to comparison and adaptation from others. The dissertation employs a mixed-methods approach. As a plausibility probe, I employ econometric analysis to test for such patterns. I constructed an original dataset of 72 countries over 6 years and approximately 25 variables. To address data missingness, I employed multiple imputation techniques. I find statistically and substantively significant relationships and patterns, as above, indicating fur- ther exploration of the underlying processes. I then test the theory via process-tracing case study comparisons of Mexi- can and Botswanan HIV/AIDS policy development over the last two decades. Drawing on written accounts, periodical articles, government documents, and oral interviews, I examine how the availability, management, and application of information affected the policies pursued. In Mexico, two factors have helped to drive success: first, the set of organizations working on HIV/AIDS policy are or- ganized as a loose network with the specialized government HIV agency serving as the hub of decisions and information exchange; second, a stable (but open) set of people have participated over the whole period. Botswana’s success has been more mixed; although its very high adult prevalence levels contribute, organiza- tional learning factors also play a role. HIV policy response actors are arranged anarchically and there have been multiple centers of authority. This has detracted from the ability to prospect and identify relevant information and then draw ac- tionable conclusions. Complete date fields below for: time period covered; and date of collection

This view of the Prevention Agenda Partner Contact Information: 2013 dataset contains the partners working on the prevention agenda priority area ,"Prevent HIV, STDs, Vaccine Preventable Diseases and Healthcare Associated Infections." The dataset is organized by county, priority area and focus area. Each partner's address, phone number and in many cases e-mail contact are provided. The Prevention Agenda 2013-17 is New York State’s health improvement plan for 2013 through 2017. This plan involves a unique mix of organizations including local health departments, health care providers, health plans, community based organizations, advocacy groups, academia, employers as well as state agencies, schools, and businesses whose activities can influence the health of individuals and communities and address health disparities. This unprecedented collaboration is designed to demonstrate how communities across the state can work together to improve the health and quality of life for all New Yorkers.The purpose of the dataset is to provide the public, health providers and tentative DOH partners with some basic information about who in NYS is working on prevention agenda related items. For more information check out http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/. The "About" tab contains additional details concerning this dataset.

There are two datasets related to the State Level Prevention Agenda Tracking Indicators posted on this site. Each dataset consists of 96 state-level health tracking indicators and sub-indicators for the Prevention Agenda 2013-2018: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Priority areas include Chronic Disease; Health and Safe Environment; Healthy Women, Infants and Children; Mental Health and Substance Abuse; and HIV, STDs, Vaccine Preventable Diseases and Healthcare Associated Infections. The most recent year dataset includes the most recent state level data for all indicators. The trend dataset includes the most recent state level data and historical data, where available. Each dataset also includes the Prevention Agenda 2018 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among racial, ethnic, and socioeconomic groups and persons with disabilities.

This dataset represents self‐reported performance data by HIV ambulatory care programs. All HIV ambulatory programs throughout New York State with a significant HIV caseload (a total caseload of at least 30 HIV‐infected patients receiving ambulatory HIV care at one or more sites) are expected to self‐report their annual quality of care performance data using standardized submission tools and methodologies. With the assistance of the online eHIVQUAL application, performance data results are instantly available to HIV programs, allowing them to immediately utilize their data findings to prioritize upcoming quality activities, and are available for generating benchmarking reports across New York State. See Limitations regarding redaction of small‐population data.

This is one of the three datasets related to the Prevention Agenda Tracking Indicators state level data posted on this site. Each dataset consists of 58 state-level health tracking indicators and 31 sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Priority areas include Chronic Disease; Health and Safe Environment; Healthy Women, Infants and Children; Mental Health and Substance Abuse; and HIV, STDs, Vaccine Preventable Diseases and Healthcare Associated Infections. The most recent year dataset includes the most recent state level data for all indicators. The trend dataset includes the most recent state level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among racial, ethnic, and socioeconomic groups and persons with disabilities. For more information, check out: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/ and https://www.health.ny.gov/PreventionAgendaDashboard, or go to the “About” tab.

This dataset is one of three Prevention Agenda Tracking Indicators posted on this site. To access the Prevention Agenda Dashboard, visit: https://www.health.ny.gov/PreventionAgendaDashboard. Each dataset consists of 58 state-level health tracking indicators and 31 sub-indicators for the Prevention Agenda 2013-2017: New York State’s Health Improvement Plan. A health tracking indicator is a metric through which progress on a certain area of health improvement can be assessed. The indicators are organized by the Priority Area of the Prevention Agenda as well as the Focus Area under each Priority Area. Priority areas include Chronic Disease; Health and Safe Environment; Healthy Women, Infants and Children; Mental Health and Substance Abuse; and HIV, STDs, Vaccine Preventable Diseases and Healthcare Associated Infections. The latest data dataset includes the most recent state level data for all indicators. The trend dataset includes the most recent state level data and historical data, where available. Each dataset also includes the Prevention Agenda 2017 state targets for the indicators. Sub-indicators are included in these datasets to measure health disparities among racial, ethnic, and socioeconomic groups and persons with disabilities. Read more about the Prevention Agenda at: http://www.health.ny.gov/prevention/prevention_agenda/2013-2017/.

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding. The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine. The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables. MAIN RESULTS Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level. Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time. Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion. Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively). HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing. Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis. Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care. Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women. Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

Abstract

The Sudan Household Health Survey 2nd round (SHHS2) 2010 provides up-to-date information on the situation of children and women and measures of key indicators that allow countries to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

The raw survey data provided by the Statistical Office were then harmonized by the Economic Research Forum, to create a comparable version with the 2006 Household Health Survey in Sudan. Harmonization at this stage only included unifying variables' names, labels and some definitions. See: Sudan 2006 & 2010- Variables Mapping & Availability Matrix.pdf provided in the external resources for further information on the mapping of the original variables on the harmonized ones, in addition to more indications on the variables' availability in both survey years and relevant comments.

The sample harmonized and disseminated by the Economic research represents Northern Sudan only.

Geographic coverage

The Sudan Household Health Survey (SHHS) 2010 dataset covers the states of Northern Sudan only (Northern, River Nile, Red Sea, Kassala, Gedarif, Khartoum, Gezira, White Nile, Sinnar, Blue Nile, North Kordofan, South Kordofan, North Darfur, West Darfur and South Darfur).

Analysis unit

1- Household/family. 2- Individual/person. 3- Woman. 4- Child.

Universe

The target universe for the SHHS includes the households and members of individual households, including nomadic households camping at a location/place at the time of the survey. The population living in institutions and group quarters such as hospitals, military bases and prisons, were excluded from the sampling frame.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

Five sets of questionnaires were used in the Sudan Household Health Survey. The first three questionnaires are based on the MICS3 and PAPFAM model questionnaires. Those three were subject to harmonization.

1) Household questionnaire which was used to collect information on all de jure household members and the household. It included the following modules: - Household information panel - Household listing - Education - Female Genital Mutilation - Chronic diseases & injuries (Northern States only) - Tobacco use (Northern States only) - Child disability - Water and sanitation - Household characteristics - Insecticide treated nets - Salt iodization

2) Women's questionnaire administered to all women aged 15-49 years in each household. It included the following modules: - Women's information panel - Women's background - Child mortality - Desire for last birth - Maternal and newborn health - Illness symptoms - Contraception - Unmet need - Marriage and union - HIV/AIDS
- Birth history - Female Genital Mutilation - Attitudes towards domestic violence - Sexual behavior STIs (Southern States only)

3) Under-five questionnaire administered to mothers. In case the mother was not listed in the household list/roster, a primary caretaker for the child was identified and interviewed. The Questionnaire for Children under Five included the following modules: - Under-five children information panel - Birth registration - Vitamin A supplementation - Breastfeeding - Care of illness - Immunization - Malaria - Anthropometry

4) Men's questionnaire administered to all men aged 15-49 years in each household. It included the following modules: - Men information panel - Men's background Marriage - Circumcision - Condom - Sexual behavior STIs - HIV/AIDS

5) Food Security Questionnaire which included the following modules: - Food security information panel - Income sources - Expenditures - Food consumption and dietary diversity

In addition to the administration of questionnaires, fieldwork teams tested the salt used for cooking in the households for iodine content, and measured the weights and heights of children under five years of age.

Cleaning operations

---> Harmonized Data:

• The SPSS package is used to harmonize the SHHS 2010 with SHHS 2006.
• The harmonization process starts with raw data files received from the Statistical Office.
• A program is generated for each dataset to create harmonized variables.
• Data is saved on the household, individual, women, as well as the children level, in SPSS and then converted to STATA, to be disseminated.

Response rate

Of the 15,000 households selected for the sample, 14,778 were successfully interviewed, yielding a response rate of 99 percent. Of the 18,614 women (age 15-49 years) identified in the selected households, 17,174 were successfully interviewed, yielding a response rate of 91.4 percent. Of the 13,587 children under age five listed in the households, questionnaires were completed for 13,282 children, which correspond to a response rate of 96.8 percent.

NNDSS - Table I. infrequently reported notifiable diseases - 2014.In this Table, provisional cases of selected infrequently reported notifiable diseases (<1,000 cases reported during the preceding year) are displayed. Note:These are provisional cases of selected national notifiable diseases, from the National Notifiable Diseases Surveillance System (NNDSS). NNDSS data reported by the 50 states, New York City, the District of Columbia, and the U.S. territories are collated and published weekly as numbered tables printed in the back of the Morbidity and Mortality Weekly Report (MMWR). Cases reported by state health departments to CDC for weekly publication are provisional because of ongoing revision of information and delayed reporting. Case counts in these tables are presented as they were published in the MMWR issues. Therefore, numbers listed in later MMWR weeks may reflect changes made to these counts as additional information becomes available. Footnote:-: No reported cases N: Not reportable NN: Not Nationally Notifiable Cum: Cumulative year-to-date counts. * Case counts for reporting year 2014 are provisional and subject to change. For further information on interpretation of these data, see http://wwwn.cdc.gov/nndss/document/ProvisionalNationaNotifiableDiseasesSurveillanceData20100927.pdf. ��� Calculated by summing the incidence counts for the current week, the 2 weeks preceding the current week, and the 2 weeks following the current week, for a total of 5 preceding years. The total sum of incident cases is then divided by 25 weeks. Additional information is available at http://wwwn.cdc.gov/nndss/document/5yearweeklyaverage.pdf. �� Not reportable in all states. Data from states where the condition is not reportable are excluded from this table except starting in 2007 for the Arboviral diseases, STD data, TB data, and influenza-associated pediatric mortality, and in 2003 for SARS-CoV. Reporting exceptions are available at http://wwwn.cdc.gov/nndss/document/SRCA_FINAL_REPORT_2006-2012_final.xlsx. �� Includes both neuroinvasive and nonneuroinvasive. Updated weekly from reports to the Division of Vector-Borne Infectious Diseases, National Center for Zoonotic, Vector-Borne, and Enteric Diseases (ArboNET Surveillance). Data for West Nile virus are available in Table II. ** Data for H. influenzae (all ages, all serotypes) are available in Table II. ��ʉ�� Updated weekly from reports to the Influenza Division, National Center for Immunization and Respiratory Diseases. Please refer to the MMWR publication for weekly updates to the footnote for this condition. ���� Please refer to the MMWR publication for weekly updates to the footnote for this condition. ���� Data for meningococcal disease (all serogroups) are available in Table II. *** Please refer to the MMWR publication for weekly updates to the footnote for this condition. ��ʉ�ʉ�� Please refer to the MMWR publication for weekly updates to the footnote for this condition. ������ Updated weekly from reports to the Division of STD Prevention, National Center for HIV/AIDS, Viral Hepatitis, STD, and TB Prevention. ������ Please refer to the MMWR publication for weekly updates to the footnote for this condition. See Table II for Dengue Hemorrhagic Fever.More information on NNDSS is available at http://wwwn.cdc.gov/nndss/.

ABSTRACT Objective: To learn the epidemiological characteristics of HIV infection in pregnant women. Method: Descriptive study with quantitative approach. The study population was composed of pregnant women with HIV/AIDS residing in the state of Alagoas. Data were organized into variables and analyzed according to the measures of dispersion parameter relevant to the arithmetic mean and standard deviation (X ± S). Results: Between 2007 and 2015, 773 cases of HIV/AIDS were recorded in pregnant women in Alagoas. The studied variables identified that most of these pregnant women were young, had low levels of education and faced socioeconomic vulnerability. Conclusion: It is necessary to include actions aimed at increasing the attention paid to women, once the assurance of full care and early diagnosis of HIV are important strategies to promote adequate treatment adherence and reduce the vertical transmission.

In 2021, 1.9 million people in Nigeria were living with HIV. Women were the most affected group, counting 1.1 thousand individuals. Also, children up to age 14 who were HIV positive equaled 170 thousand.

ObjectiveGeographic and racial disparities may contribute to variation in the incidence and outcomes of HIV-associated cancers in the United States.MethodUsing the Surveillance, Epidemiology, and End Results (SEER) database, we analyzed Kaposi sarcoma (KS) incidence and survival by race and geographic region during the combined antiretroviral therapy era. Reported cases of KS in men from 2000 to 2013 were obtained from 17 SEER cancer registries. Overall and age-standardized KS incidence rates were calculated and stratified by race and geographic region. We evaluated incidence trends using joinpoint analyses and calculated adjusted hazard ratios (aHR) for overall and KS-specific mortality using multivariable Cox proportional hazards models.ResultsOf 4,455 KS cases identified in men younger than 55 years (median age 40 years), the annual percent change (APC) for KS incidence significantly decreased for white men between 2001 and 2013 (APC -4.52, p = 0.02). The APC for AA men demonstrated a non-significant decrease from 2000–2013 (APC -1.84, p = 0.09). Among AA men in the South, however, APC has significantly increased between 2000 and 2013 (+3.0, p = 0.03). In addition, compared with white men diagnosed with KS during the same time period, AA men were also more likely to die from all causes and KS cancer-specific causes (aHR 1.52, 95% CI 1.34–1.72, aHR 1.49, 95% CI 1.30–1.72 respectively).ConclusionAlthough overall KS incidence has decreased in the U.S., geographic and racial disparities in KS incidence and survival exist.