The database compiles aggregated time series data from the National Center for Epidemiology, Disease Prevention, and Control (CDC) of Peru. This comprehensive national data, collected annually, originates from the National Epidemiological Surveillance Network and the system's reporting channels. Time series have been constructed in parallel, covering the period from 1983 to 2023.

This dataset contains surveillance study estimates for population size, HIV prevalence, and ART coverage among female sex workers (FSW), men who have sex with men (MSM), people who inject drugs (PWID), and transgender men and women (TGM/W) from 2010-2023. It was created to support the UNAIDS Estimates Key Population Workbook for use by HIV estimates teams in sub-Saharan Africa. Key population surveillance reports, including Ministry of Health-led biobehavioural surveys, mapping studies, and academic studies were used to populate the database.

The dataset was populated using existing key population size estimate databases including:

UNAIDS Key Population Atlas

US Centers for Disease Control and Prevention surveillance database

Global Fund against HIV/AIDS, TB, and Malaria surveillance database

Global.HIV database

Systematic review databases among MSM (Stannah et al, 2019 and Stannah et al., 2023) and PWID (Degenhardt et al., 2023)

and was additionally supplemented by a literature review of peer-reviewed and grey literature sources.

The data can be explored in this web application and the accompanying manuscript can be found here

Abstract

Sierra Leone has just emerged from a ten- year civil war that significantly reduced the standard of living, and access to food for many people. The large scale destruction of most of the health and other social infrastructure that took place during the war intensified the problem of health service delivery and exacerbated poverty. A poor and undernourished population is easily susceptible to various diseases. The Civil conflict that ended in 2002 may have increased the risk for human immunodeficiency virus (HIV) transmission through the sexual abuse of teenage girls and women, drug abuse, migration, and displacement of the population. In addition, the problem of the spread of the disease is compounded by the low level of awareness and knowledge about HIV/AIDS particularly knowledge relating to its mode of transmission and methods of protection.

Recognizing the threat posed by the spread of HIV/AIDS, the government of Sierra Leone established the National HIV/AIDS Secretariat (NAS) as the main institution responsible for the development and implementation of effective strategies and programs geared towards the prevention and control of the spread of HIV/AIDS.

NAS commissioned Statistics Sierra Leone to undertake this first nationwide behavioural surveillance survey aimed at providing baseline data for use in designing behavioural change programs. The primary objective of this sentinel surveillance has been to provide national estimates on key indicators related to HIV prevention and infection for use in the development of a national database on HIV/AIDS in Sierra Leone.

The HIV/AIDS behavioural surveillance survey was carried out in 206 enumeration areas (EAs) used in the Sierra Leone Integrated Household survey (SLIHS for which comprehensive household listings existed. One locality within each selected EA was randomly selected. Using cumulative probability proportional to size sampling, fifteen and twenty households were selected for rural and urban EAs respectively. To reduce sample shortfall likely to arise due to migration, death etc. of the selected households, five replacement households were selected for both rural and urban EAs. In each selected household, one adolescent and one adult were interviewed. A total of 5374 respondents between the ages of 15-49 years were interviewed comprising 47 per cent males and 53 per cent females. In the households with more than one eligible respondent, use was made of the “Kish Selection Table” of random numbers to choose the member of the household to be interviewed. This procedure was adopted to reduce bias in the selection of respondents.

Geographic coverage

National Coverage

Analysis unit

At District Level: The units of analysis for the survey were the selected households. In each household, one Adolescent and one adult "Female" (15-49) was selected.

Universe

Selected EA's

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling for the BSS study followed the methodology used in the SLIHS (see annex).The BSS study was carried out in 206 EAS used in the SLIHS for which comprehensive household listings existed. Twenty EAs used in the SLIHS were unavailable which represented a shortfall of 8.4% of the original target sample size. The number of households interviewed in the urban and rural EAs was determined base on SLIHS methodology. Fifteen rural households and twenty urban households were targeted. One locality within each selected EA was randomly selected. The total number of persons in each of the selected EAs was added cumulatively for the entire locality and a sampling interval was fixed. Using a table of random numbers a number between one and the sampling interval was selected as starting household and subsequent households were selected by adding the fixed sampling interval. To reduce sample shortfall likely to arise due to migration, death etc. of the selected households, five replacement households were selected for both rural and urban EAs.

Sampling deviation

To minimize cost it was decided to repeat the study in the EAs used for the Sierra Leone Integrated Household Survey (SLIHS, 2003/2004). The SLIHS sample was representative of all the administrative districts, chiefdoms or wards in Sierra Leone and comprehensive and updated household listings existed for this sample of EAs. It was intended to carry out the study in all the EAs used in the SLIHS. However, the study was conducted in 206 EAs. Twenty EAs used in the SLIHS were unavailable which represented a shortfall of 8.4% of the original target sample size. The number of households interviewed in the urban and rural EAs was determined based on SLIHS methodology. Fifteen rural households and twenty urban households were targeted. One locality within each selected EA was randomly selected with probability proportional to size, using the number of listed households as size measure. The total number of households in each of the selected locality was added cumulatively for the entire locality and a sampling interval was fixed. Using a table of random numbers a number between one and the sampling interval was selected as starting household and subsequent households were selected by adding the fixed sampling interval. To reduce sample shortfall likely to arise due to migration, death etc. of the selected households, five replacement households were selected for both rural and urban EAs.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument that was used was the standard questionnaire, which included standardized UNAIDS indicators and also National HIV/AIDS Secretariat indicators which covered STI/HIV knowledge, risk perception, sexual and health-seeking behaviour. However, some questions were simplified or shortened and others were adjusted to suit local circumstances. The questionnaire consisted of sections about demographic characteristics of the household, Knowledge, opinions, behaviour and attitudes regarding sexually transmitted infections (STIs) and HIV/AIDS, sexual behaviour and condom use.

Cleaning operations

Completed questionnaires were verified and coded in Freetown by a team of five coders and one supervisor. The coding team checked each questionnaire to ensure that it was properly filled out. The questionnaires were then handed over to the Data Processing Division for processing. The IMPS software program was used to enter the data, which was transferred to SPSS for analysis.

Response rate

In each selected household, one adolescent and one adult were interviewed. A total of 5374 respondents between the ages of 15-49 years were interviewed comprising 47 percent males and 53 percent females.

Sampling error estimates

The Sierra Leone General Population HIV/AIDS Behavioural Surveillance Survey 2004 sampling frame was based on the 2003/2004 Sierra Leone Integrated Household Survey (SLIHS). The sample error was estimated at 5%.

Data appraisal

Other forms of data appraisal included data verification and coding.

Sciensano has been responsible for monitoring HIV and AIDS in Belgium since 1985. This surveillance makes it possible to assess the number of people infected with HIV and the proportion of those with AIDS.

The epidemiological surveillance of HIV in Belgium is based on several data collections carried out by Sciensano. National data are collected from the HIV reference centres (HRCs) and AIDS reference laboratories (ARLs): a) National data collection of all HIV diagnosed patients in Belgium; b) National data collection of all HIV patients in care, through an exhaustive data collection of all viral load measures performed in Belgium and a data collection of demographic, biological, immunological, treatment and death data of patients in care in the HRCs (around 80 % of all patients in care in Belgium); c) A laboratory data collection on viro-immunological follow-up of all new-borns from HIV positive mothers; d) A national data collection of post-exposure prophylaxis episodes. Since the beginning of the HIV epidemic, this surveillance enables the monitoring of the trends in number of people diagnosed with HIV and number of patients in medical follow-up, as well as to identify certain socio-demographic factors associated with the risk of HIV infection or of a pejorative clinical outcome. This information supports health authorities and HIV stakeholders to decide on evidence-based HIV prevention and care strategies and define target groups for tailored interventions. Statbel, the Belgian statistical office collects, produces and disseminates reliable and relevant figures on the Belgian economy, society and territory. The collection is based on administrative data sources and surveys. This project aims to link the HIV surveillance data with selected Statbel information. This will permit to greatly improve the quality of the HIV surveillance data by completing the data already collected by Sciensano with additional socio-economic and socio-demographic information on patients profiles, filling in missing data in the Sciensano database with demographics from Statbel, ascertaining vital status of lost-to-follow-up patients and completing the information on causes of death. Additionally, a linkage with the new-born registry would permit to have more demographic and clinical information on children born from HIV-positive women.

The purpose of the investigation is to provide information on selected venereal diseases to assess the development of the epidemiological situation in the Czech Republic, to monitor the health status of the population and to manage the health care provided. The results are forwarded to the World Health Organization and ECDC.

RPN is a continuous continuation of long-term statistical monitoring (since 1959) in the ÚZIS CR. Since 2003, RPN has been operated as a web application with a central database. Regional hygiene stations enter data into the register via an internet connection via the secure https protocol. Access to the registry and assignment of a user role is approved by the administrator.

RPN - its internet part - is established by the Ministry of Health of the Czech Republic and is part of the hygiene service information system. The hygiene service is responsible for the protection of public health and contributes to the fulfillment of Act No. 258/2000 Coll. , on the protection of public health and Decree of the Ministry of Health No. 306/2012 Coll. , on the prevention of the emergence and spread of infectious diseases and hygienic requirements for the operation of medical facilities and social care institutions.

Statistical unit of inquiry

A statistical unit is each detected sexually transmitted disease that is subject to reporting, including reinfections. The register includes all epidemiological reports on venereal disease, on death with venereal disease, suspected disease or transmission of venereal disease, and marked sources of venereal disease infection, including cases detected in foreigners.

The following diseases are subject to mandatory reporting of venereal diseases:

Congenital syphilis (A50), early syphilis (A51), late syphilis (A52), other and unspecified syphilis (A53), gonococcal infection (A54), lymphopogranuloma venereum (A55), chancroid-ulcer molle (A57).

The dataset “Impact of HIV strategies for MSM” contains data obtained from an agent-based model. The model follows the sexual life of 20,000 men who have sex with men (MSM) in the Netherlands. Via sexual contacts, men may get infected with HIV or N. Gonorrhoeae (NG). The model simulates sexual behaviour, demography, and the course of HIV or NG infection (for those who have been infected). The data from the model are therefore data of “fictitious” (simulated) individuals, not of real individuals. The course of HIV infection was modelled using data from the national database of HIV-positive individuals in the Netherlands (Source: Stichting HIV Monitoring). Parameters relating to sexual behaviour were obtained from data from the Amsterdam Cohort Study and the Network Study among MSM in Amsterdam. The model was calibrated to data on annual HIV diagnoses in 2008-2014 (from Stichting HIV Monitoring) and gonorrhoea positivity in 2009-2014 (data obtained from the National Database of STI Clinics in the Netherlands (SOAP)). Model outcomes include the annual numbers of MSM getting infected with HIV; HIV-positive MSM getting diagnosed, entering care, or starting treatment; MSM developing AIDS; MSM getting infected with NG; MSM treated for gonorrhoea; HIV tests, NG tests, etc. With the model, we calculated these numbers for the years 2018-2027, for the situation with the current testing rates and without PrEP. Subsequently we calculated these numbers with increased HIV/STI testing: a small, a moderate, and a high increase in testing among all MSM or only among MSM in specific subgroups of MSM. Finally, the calculations were repeated accounting for a nationwide PrEP programme for MSM at high risk to acquire HIV.

ObjectiveDelayed diagnosis of HIV can lead to an inappropriate response to antiretroviral therapy (ART), rapid progression of the disease, and death. It can also carry harmful effects on public health due to the increment of transmission. This study aimed to estimate the duration of delayed diagnosis (DDD) in HIV patients in Iran.MethodsThis hybrid cross-sectional cohort study was conducted on the national HIV surveillance system database (HSSD). Linear mixed effect models with random intercept, random slope, and both were used to estimate the parameters required for the CD4 depletion model to determine the best-fitted model for DDD, stratified by the route of transmission, gender, and age group.ResultsThe DDD was estimated in 11,373 patients including 4,762 (41.87%) injection drug users (IDUs), 512 (4.5%) men who had sexual contact with men (MSM), 3,762 (33.08%) patients with heterosexual contacts, and 2,337 (20.55%) patients who were infected through other routes of HIV transmission. The total mean DDD was 8.41 ± 5.97 years. The mean DDD was 7.24 ± 0.08 and 9.43 ± 6.83 years in male and female IDUs, respectively. In the heterosexual contact group, DDD was obtained as 8.60 ± 6.43 years in male patients and 9.49 ± 7.17 years in female patients. It was also estimated as 9.37 ± 7.30 years in the MSM group. Furthermore, patients infected through other transmission routes were found with a DDD of 7.90 ± 6.74 years for male patients and a DDD of 7.87 ± 5.87 years for female patients.ConclusionA simple CD4 depletion model analysis is represented, which incorporates a pre-estimation step to determine the best-fitted linear mixed model for calculating the parameters required for the CD4 depletion model. Considering such a noticeably high HIV diagnostic delay, especially in older adults, MSM, and heterosexual contact groups, regular periodic screening is required to reduce the DDD.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and autonomous reporting entities, including New York City and the District of Columbia (D.C.), as well as U.S. territories and affiliates. On April 5, 2020, COVID-19 was added to the Nationally Notifiable Condition List and classified as “immediately notifiable, urgent (within 24 hours)” by a Council of State and Territorial Epidemiologists (CSTE) Interim Position Statement (Interim-20-ID-01). CSTE updated the position statement on August 5, 2020, to clarify the interpretation of antigen detection tests and serologic test results within the case classification (Interim-20-ID-02). The statement also recommended that all states and territories enact laws to make COVID-19 reportable in their jurisdiction, and that jurisdictions conducting surveillance should submit case notifications to CDC. COVID-19 case surveillance data are collected by jurisdictions and reported voluntarily to CDC.

For more information: NNDSS Supports the COVID-19 Response | CDC.

The deidentified data in the “COVID-19 Case Surveillance Public Use Data” include demographic characteristics, any exposure history, disease severity indicators and outcomes, clinical data, laboratory diagnostic test results, and presence of any underlying medical conditions and risk behaviors. All data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.

COVID-19 Case Reports

COVID-19 case reports have been routinely submitted using nationally standardized case reporting forms. On April 5, 2020, CSTE released an Interim Position Statement with national surveillance case definitions for COVID-19 included. Current versions of these case definitions are available here: https://ndc.services.cdc.gov/case-definitions/coronavirus-disease-2019-2021/.

All cases reported on or after were requested to be shared by public health departments to CDC using the standardized case definitions for laboratory-confirmed or probable cases. On May 5, 2020, the standardized case reporting form was revised. Case reporting using this new form is ongoing among U.S. states and territories.

Data are Considered Provisional

• The COVID-19 case surveillance data are dynamic; case reports can be modified at any time by the jurisdictions sharing COVID-19 data with CDC. CDC may update prior cases shared with CDC based on any updated information from jurisdictions. For instance, as new information is gathered about previously reported cases, health departments provide updated data to CDC. As more information and data become available, analyses might find changes in surveillance data and trends during a previously reported time window. Data may also be shared late with CDC due to the volume of COVID-19 cases.
• Annual finalized data: To create the final NNDSS data used in the annual tables, CDC works carefully with the reporting jurisdictions to reconcile the data received during the year until each state or territorial epidemiologist confirms that the data from their area are correct.
• Access Addressing Gaps in Public Health Reporting of Race and Ethnicity for COVID-19, a report from the Council of State and Territorial Epidemiologists, to better understand the challenges in completing race and ethnicity data for COVID-19 and recommendations for improvement.

Data Limitations

To learn more about the limitations in using case surveillance data, visit FAQ: COVID-19 Data and Surveillance.

Data Quality Assurance Procedures

CDC’s Case Surveillance Section routinely performs data quality assurance procedures (i.e., ongoing corrections and logic checks to address data errors). To date, the following data cleaning steps have been implemented:

• Questions that have been left unanswered (blank) on the case report form are reclassified to a Missing value, if applicable to the question. For example, in the question “Was the individual hospitalized?” where the possible answer choices include “Yes,” “No,” or “Unknown,” the blank value is recoded to Missing because the case report form did not include a response to the question.
• Logic checks are performed for date data. If an illogical date has been provided, CDC reviews the data with the reporting jurisdiction. For example, if a symptom onset date in the future is reported to CDC, this value is set to null until the reporting jurisdiction updates the date appropriately.
• Additional data quality processing to recode free text data is ongoing. Data on symptoms, race and ethnicity, and healthcare worker status have been prioritized.

Data Suppression

To prevent release of data that could be used to identify people, data cells are suppressed for low frequency (<5) records and indirect identifiers (e.g., date of first positive specimen). Suppression includes rare combinations of demographic characteristics (sex, age group, race/ethnicity). Suppressed values are re-coded to the NA answer option; records with data suppression are never removed.

For questions, please contact Ask SRRG (eocevent394@cdc.gov).

Additional COVID-19 Data

COVID-19 data are available to the public as summary or aggregate count files, including total counts of cases and deaths by state and by county. These

PrEP is the use of an antiretroviral medication by people who are uninfected to prevent the acquisition of HIV. The efficacy of PrEP has been shown in a number of randomised controlled trials including iPREX, Partners PrEP, PROUD and ANRS-IPERGAY. In 2015, the European Centre for Disease Prevention and Control (ECDC) recommended that European Union (EU) and European Economic Area (EEA) countries should consider integrating PrEP into their existing HIV prevention package for those most at risk of HIV infection, starting with men who have sex with men (MSM). This was followed by the World Health Organization (WHO) recommendations that PrEP should be offered as an additional prevention option to all people at substantial risk of HIV infection as part of combination prevention approaches. As a result, several countries in the EU/EEA have either implemented PrEP or are considering options for implementation. Since the 1st of June 2017, PrEP is nationally available in Belgium and reimbursed for people who are at increased risk for HIV acquisition. Belgium is one of the countries in Europe reporting a high HIV incidence, with 8.1 new HIV infections per 100 000 inhabitants in 2019.The epidemic mainly affects two populations: men who have sex with men (MSM) and Sub-Saharan African migrants, most of whom have acquired HIV through unprotected heterosexual contacts. A recent study suggests that ongoing clustered transmission in Belgium is almost exclusively driven by MSM. As the national PrEP program is brought to scale, the need for a robust monitoring system emerges. An effective PrEP program is one in which people in greatest need of HIV prevention are appropriately identified, offered PrEP, and then continue to receive continued support to use PrEP as needed. Monitoring PrEP program implementation is therefore important to (1) track progress in uptake and coverage among the eligible population, (2) estimate impact on the HIV epidemic, and (3) inform the strategic planning of the program (e.g. cost, resources, supply of commodities).

The Department of Defense Health Agency’s (DHA) Vision Center of Excellence (VCE) analyzed data from the MHS MART database on behalf of the VEHSS project. MHS MART is a data management and reporting system used to support decision-making, health care analysis, and operational reporting. MART integrates various sources within MHS to provide a centralized repository for health care data, facilitating access to information that aids in managing health care services, resources, and performance across MHS. Data are based on claims and encounter records in the MHS Management Analysis and Reporting Tool (MART) database. The population includes all active-duty and retired military members and their dependents in the MHS. The sample size is approximately 9.08 million persons. These data are also available in the VEHSS Data Explorer, an interactive data visualization tool reporting prevalence information from more than 10 data sources: https://www.cdc.gov/vision-health-data/index.html

Users can obtain demographic, maternal and child health, and development information regarding countries with whom UNICEF works. BackgroundUNICEF (United Nations Children Fund) is a non-governmental agency that works with governments, national and international agencies and communities to reduce the cycle of poverty in more than 150 countries and territories. UNICEF’s programs address child health and nutrition, safe water and sanitation, gender-equitable quality basic education, and the protection of children from violence, exploitation and AIDS. Topics include: country demographics, basic health, HIV/AIDS , education, and economics, among others. User Functionality Users can obtain information regarding countries with whom UNICEF works. UNICEF provides a background of each country; indicators such as country demographics, basic health, HIV/AIDS, nutrition, education, economics, child protection, women’s health and status, and rate of progress; information regarding UNICEF initiatives in the country; and news regarding current events relevant to the well-being of its residents, the impact of UNICEF and similar programs, as well as the impact of epidemics, natural disaster and political conflicts on children. Users can obtain country summaries and health indicators for each country. Maps can be downloaded as PDF files. Data Notes Data are derived from multiple sources including: UNICEF, the United Nations Population Division, United Nations Statistics Division, Joint United Nations Programme on HIV/AIDS (UNAIDS), United Nations Educational, Scientific and Cultural Organization (UNESCO), UNESCO Institute of Statistics (including Demographic Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS), Education for All 2000 Assessment), World Health Organization (WHO), Report on the Global HIV/ AIDS Epidemic, US Census Bureau, HIV/AIDS Surveillance Database, Behavioral Surveillance Surveys (BSS), Reproductive Health Surveys (RHS), International Telecommunications Union, Yearbook of Statistics, Children on the Brink, World Bank, International Monetary Fund (IMF), Organization for Economic Co-operation and Development (OECD) and national household surveys and routine reporting systems. Years to which the data apply is noted with the relevant indicator. Data updates vary according to indicator, and can be found under the “Definitions and Data Source” tab. Information is available on the country level.

A full anonymized data set was collected as a part of the ICU infection control and surveillance program; 01/01/2011-12/31/2020

File "Zenodo_DB_v4.csv" contains daily data (one row is one day) on infection surveillance ordered by date.

File "Data_Dictionary_MainDB_2021.csv" contains the description of all variables from the data set.

The Stanford University HIV Drug Resistance Database is a curated public database designed to represent, store, and analyze the different forms of data underlying HIVs drug resistance. HIVDB has three main types of content: (1) Database queries and references, (2) Interactive programs, and (3) Educational resources. Database queries are designed primarily for researchers studying HIV drug resistance. The interactive programs and educational resources are designed for both researchers and those wishing to learn more about HIV drug resistance. 1.DATABASE QUERY AND REFERENCE PAGES Genotype-Treatment Correlations This Genotype-Treatment section of the database links to 15 interactive query pages that explore the relationship between treatment with HIV-1 antiretroviral drugs (ARVs) and mutations in HIV reverse transcriptase (RT), protease, and integrase. There are five types of interactive query pages: Treatment Profiles (Protease and RT inhibitors) Mutation Profiles (Protease and RT mutations) Detailed Treatment Queries (Protease, RT, and integrase inhibitors) Detailed Mutation Queries (Protease, RT, and integrase mutations) Mutation Prevalence According to Subtype and Treatment Genotype-Phenotype Correlations The main page of the Genotype-Phenotype Correlations section links to four interactive query pages: three dynamically updated data summaries and one regularly updated downloadable dataset. Drug Resistance Positions Query for levels of resistance associated with known drug resistance mutations Detailed Phenotype Queries Queries for levels of resistance associated with individual mutations or mutation combinations at all positions of protease, RT, and integrase Patterns of Drug Resistance Mutations Downloadable Reference Dataset Genotype-Clinical Correlations This part of the database has two main sections: Clinical Trials Datasets Summaries of Clinical Studies References This part of the database has two main sections: one with summaries of the data from each of the references in HIVDB and one in which every primate immunodeficiency virus sequence in GenBank is annotated according to its presence or absence in HIVDB. Studies in HIVDB GenBank HIVDB New Submissions Approximately every three months, the New Submissions section lists the studies that have been entered into HIVDB. The study title links to the introductory page of the study in the References section. Database Statistics (http://hivdb.stanford.edu/pages/HIVdbStatistics.html) 2. INTERACTIVE PROGRAMS HIVDB has seven main interactive programs. 1. HIVdb Program Mutation List Analysis Sequence Analysis HIVdb Output Sierra Web Service Release Notes Algorithm Specification Interface (ASI) 2. HIValg Program 3. HIVseq Program 4. Calibrated Population Resistance (CPR) tool 5. Mutation ARV Evidence Listing (MARVEL) 6. ART-AiDE 7. Rega HIV-1 Subtyping tool Three programs in the HIV Drug Resistance Database share a common code base: HIVseq, HIVdb, and HIValg. HIVseq accepts user-submitted protease, RT, and integrase sequences, compares them to the consensus subtype B reference sequence, and uses the differences as query parameters for interrogating the HIV Drug Resistance database (Shafer, D Jung, & B Betts, Nat Med 2000; Rhee SY et al. AIDS 2006). The query result provides users with the prevalence of protease, RT and integrase mutations according to subtype and PI, nucleoside RT inhibitor (NRTI), non-nucleoside RT inhibitor (NNRTI), and integrase inhibitor (INI) exposure. This allows users to detect unusual sequence results immediately so that the person doing the sequencing can check the primary sequence output while it is still on the desktop. In addition, unexpected associations between sequences or isolates can be discovered by immediately retrieving data on isolates sharing one or more mutations with the sequence. There are three ways in which the HIVdb program can be used: (i) entering a list of protease and RT mutations, (ii) entering a complete sequence containing protease, RT, and/or integrase, and (iii) using a Web Service. HIVdb is an expert system that accepts user-submitted HIV-1 pol sequences and returns inferred levels of resistance to 20 FDA-approved ARV drugs including 8 PIs, 7 NRTIs, 4 NNRTIs, and - with this update - one INI. In the HIVdb system, each HIV-1 drug resistance mutation is assigned a drug penalty score and a comment; the total score for a drug is derived by adding the scores of each mutation associated with resistance to that drug. Using the total drug score, the program reports one of the following levels of inferred drug resistance: susceptible, potential low-level resistance, low-level resistance, intermediate resistance, and high-level resistance. HIValg is designed for users interested in comparing the results of different algorithms or who are interested in comparing and evaluating existing and newly developed algorithms. The ability to develop new algorithms that can be run on the HIV Drug Resistance Database depends on the Algorithm Specific Interface (ASI) compiler (Shafer & Betts JCM 2003). Submission of Sequences and Mutations For each of the three programs, sequences can be entered using either the Sequence Analysis Form or the Mutation List form. 3. EDUCATIONAL RESOURCES HIVDB contains several regularly updated sections summarizing data linking RT, protease, and integrase mutations and antiretroviral drugs (ARVs). These sections include (i) tabular summaries of the major mutations associated with each ARV class, (ii) detailed summaries of the major, minor, and accessory mutations associated with each ARV, (iii) the comments used by the HIVdb program, (iv) the scores used by the HIVdb program, (v) clinical studies in which baseline drug resistance mutations have been correlated with the virological response (clinical outcome) to a specific ARV, (vi) mutations that can be used for drug resistance surveillance, and (vii) a two-page PDF handout. 1. Drug Resistance Summaries Tabular Drug Resistance Summaries by ARV Class Detailed Drug Resistance Summaries by ARV Drug Resistance Mutation Comments Used by the HIVdb Program Drug Resistance Mutation Scores Used by the HIVdb Program Genotype-Clinical Outcome Correlation Studies 2. Surveillance Drug-Resistance Mutation List Section 3. PDF Handout Grant Support 1. National Institute for Allergy and Infectious Diseases (NIAID, NIH): Online HIV Drug Resistance Database (PI: Robert W. Shafer, MD, 1R01AI68581-01A1), 04/01/06 - 3/31/11 2. National Institute for Allergy and Infectious Diseases (NIAID, NIH) supplement to the grant Identification of Multidrug-Resistant HIV-1 Isolates (PI: Robert W. Shafer, MD, AI46148-01): Supplement provided 1999-2005. 3. NIH/NIGMS Program Project on AIDS Structural Biology Program Project: Targeting Ensembles of Drug Resistant Protease Variants (PI: Celia Schiffer, PhD, University of Massachusetts): 2002-2007 4. University-wide AIDS Research Program (CR03-ST-524). Community collaborative award: Optimizing Clinical HIV Genotypic Resistance Interpretation: Principal Investigators: Robert W. Shafer, MD and W. Jeffrey Fessel MD (Kaiser Permanente Medical Care Program): 2004-2005 5. Stanford University Bio-X Interdisciplinary Initiative: HIV Gene Sequence Analysis for Drug Resistance Studies: A Pharmacogenetic Challenge Principal Investigators: Robert W. Shafer, MD and Daphne Koller, Ph.D. (Computer Science): 2000-2002

Comparison of measures of disease severity by birth sex.

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Abstract

The main objectives of the study were to assess the knowledge and beliefs of high-risk groups about STI and HIV, determine the prevalence of HIV infection and syphilis among these groups and obtain baseline data that will permit comparisons of risk behaviours, HIV infection and syphilis over time.

Geographic coverage

Six selected states

Analysis unit

State, group, individual

Universe

The Integrated Biological and Behavioural Surveillance Survey 2007 covered only males and females aged up to 15-49 years among seven sub-populations at risk of HIV in six selected states of Nigeria, namely Female Sex Workers (both brothel- and non-brothel-based), men who have sex with men (MSM), injecting drug users (IDU), members of the armed forces, police, and transport workers (TW).

Kind of data

Sample survey data [ssd]

Sampling procedure

In order to reach a representative sample of all groups involved in the 2007 IBBSS, a number of different sampling techniques were used depending on the group in question, including simple random sampling (SRS), cluster sampling (probability proportionate to size (PPS) for fixed populations), time-location sampling (TLS) and respondent-driven sampling (RDS). For MSM and IDU, the RDS method was used, while a TLS technique was used to select non-brothel-based FSW and TW. The brothel-based FSW, armed forces, and police were selected using a two-stage cluster sampling technique. The take all (TA) sampling method was used when the desired sample size was not attainable based on the results of target population mapping.

ITLS is a form of cluster sampling that contains both time and location dimensions. TLS provides the opportunity to reach members of a target population who access certain locations at any point in time. The process starts by creating time * location PSU (PSU that have both a time and a location dimensions) from which a random sample is selected. At the second stage all or a sub-sample of randomly selected population members who appear at the site during a designated time interval of fixed length, for example 4 hours, are interviewed. To the extent that all members of a target population access the locations at some point in time, TLS is a probability sampling method because: (i) all population members have a non-zero chance of selection as long as the TLS frame is complete; and (ii) the selection probabilities can be calculated by taking the time dimension as well as the space dimension into account.

RDS is a method that combines "snowball sampling" with a mathematical model that weights the sample to compensate for the fact that the sample was collected in a non-random way. Characterized by long referral chains (to ensure that all members of the target population can be reached) and a statistical theory of the sampling process which controls for bias including the effects of choice of seeds and differences in network size, RDS overcomes the shortcomings of institutional sampling (coverage) and snow-ball type methods (statistical validity). By making chain-referral into a probability sampling method and consequently resolving the dilemma of a choice between coverage and statistical validity, RDS has become the most appropriate method for reaching the hard-to-reach population groups. The RDS process starts with the recruitment of the initial seeds each of whom recruits a maximum of two to three members from their population group.

Sampling deviation

Cluster samples were chosen randomly based on sampling frames developed through the mapping process. This process was to identify places where potential subjects could be reached and sampled. Field work for the mapping exercise was performed over one week. Due to the limited period some hidden populations may not be adequately represented in sampling frames.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed in collaboration with FMOH, SFH, CDC, WHO, UNAIDS and other stakeholders. At both central- and state-level trainings, each question in the questionnaire was reviewed and role-played and possible challenges were identified and addressed. The questionnaire of Integrated Biological and Behavioural Surveillance Survey 2007 was grouped into fifteen sections

Section 0: Identification particularsBackground characteristics Section 1: Background characteristics Section 2: Marriage and partnerships Section 3: Sexual history numbers and types of partners Section 4: Sexual history-regular partners (for those with spouse/live-in sexual partners only; for MSM, female spouse/live-in sexual partners only) Section 5: Sexual history-boy friends/girl friends (for those with boy friends/girl friends sexual partners only; for MSM, female boy friends/girl friends sexual partners only) Section 6: Sexual history-purchasing sex (male only) (for those with commercial sex partners only; for MSM, female commercial sex partners only) Section 7: Sexual history-casual-non regular non-paying sexual partners (for those with casual sexual partners only; for MSM, female casual sexual partners only) Section 8: Selling sex (for female populatios only) Section 9: Social habits (all groups) Section 10: Dru use/needle sharing (all population reporting drug injection in the past 12 months) Section 11: MSM-men who have sex with men (ask all respondents) Section 12: STIs (ask all respondents) Section 13: Knowledge, opinions, and attitudes towards HIV/AIDS (ask all respondents) Section 12: Exposure to interventions

Cleaning operations

After data entry, the data was cleaned using STATA 10. Frequency counts were carried out to check consistency and assess cleaniness of the database. The data cleaning also included the following:

Searching for ages outside the age range criteria; Cross-checking all corresponding skips to the questionnaire; Reviewing the cluster allocations; Cross-checking the questionnaire completion responses from the interviewers in the database with the records in the supervisors log to ensure they matched; Tallying the supervisors log of blood samples collected to ensure that recorded numbers of samples collected matched the results recorded in the database; and Consistency checks involving cross-checking answers to related questions.

Response rate

There were 11,175 individuals selected for this study out of whom 0.8% and 8.1% refused to participate in behavioural and biological componenets of the study respectively.

Non-brothel based FSW had the highest refusal rate of 2.7% and 19.4% for behavioural and biological components respectively, followed by brothel-based FSW at 2.2% and 13.1% respectively. Refusal rates for the behavioural component were less than 0.5% for other groups.

For the biological component, refusal rates were 3% for police, 0.8% for the armed forces, 1 .2% for TW, 4.6% for MSM, and 3.3% for IDU.

Sampling error estimates

No sampling error estimate

Data appraisal

A template for the questionnaire was designed with pre-programmed consistency checks for cross-checking answers, including skips and eligibility criteria. Laboratory data forms were collected on a periodic basis from the central laboratories and brought to the same centralized location for data entry. At least 25% of the questionnaires entered daily by each data entry clerk had the behaviour and other non-biological data entered, while 100% double-data entry was achieved for the biological data for quality control purposes. The data entry clerks were supervised by three supervisors who reviewed and validated all questionnaires entered.

Abstract

The study aim was to describe the roles and health issues of older people (50 years and older) who have offspring who are infected or deceased due to HIV, or who have HIV themselves. In addition the effects of the introduction of HIV treatment on the lives and wellbeing of people aged 50 and above was investigated. Specifically, the aims of the study were to describe the effects on physical and mental health, household income and social situation as well as the tasks and responsibilities of older people infected and/or affected by HIV.

Geographic coverage

Rural subdistrict Hlabisa, Kwa-Zulu Natal Province, South Africa

Analysis unit

individuals

Universe

Hlabisa, Africa Centre, Health and Demographic Surveillance Site fifty plus population

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample was stratified into five groups. Group 1 was older people on HIV treatment for 1 year or more in 2010 at the time of Wave I of the project. Group 2 was older people who were not on HIV treatment or on treatment for 3 months or less in 2010 (Wave I). Group 3 was older people who had an adult (14-49 years) offspring in the household who was HIV-infected in 2010 (Wave 1). Group 4 was older people who had experienced an HIV-related death of an adult household member in 2010 (Wave 1). Group 5 was older people who were not on HIV treatment or were on treatment for 3 months or less in 2013 (at the time of Wave II). There was over sampling of participants in groups 2 and 5. A two-stage sampling process was adopted for participants in groups 1, 2 and 5. At stage one, all persons meeting the respective criteria for each group were identified from the Hlabisa treatment programme. At stage two, 100 participants for each group who are also under surveillance were randomly selected. The study is restricted to persons aged 50 and above and to those living in the Africa Centre surveillance area. The sample is representative of HIV-infected and HIV-affected older persons in the study population. Respondents who were absent, not found or refused were replaced with another randomly selected respondent meeting the same inclusion criteria. Sampling frame used was the Hlabisa HIV care and Treatment database (ARTeMIS) and the Africa Centre Longitudinal surveillance system. Participants in groups 1,2 and 5 were first identified from ARTeMIS then all those under surveillance and the specific criteria for each group were randomly selected and approached for participation.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Well-Being of Older People Study (WOPS) were based on the World Health Organization's Study on Global Ageing and Adult Health (SAGE) questionnaires, with some modifications and additions to suit the local environment. The questionnaires were also partially harmonized with a similar sub-study in Uganda. The study instrument has three main components: (1) detailed questionnaire on basic demographic information, description of health state including functional ability assessment, well-being, health problems and symptoms, health care utilisation, care giving and care receiving, and experiences of living with HIV (2) collection of anthropometry data (3) blood sample for laboratory measured health risk biomarkers

Cleaning operations

Data editing and quality control was conducted at three levels. 1. During field work the professional nurses cross checked their forms for incomplete or missing information. 2. The two co-principal investigators checked each form for completeness and quality of data. 3. Data entry constraints were built into the data entry programme to spot errors and inconsistencies. Any errors identified at any of these stages were referred back to the professional nurses who revisited the participant for data correction.