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;

United States US: Incidence of HIV: per 1,000 Uninfected Population data was reported at 0.110 Ratio in 2019. This stayed constant from the previous number of 0.110 Ratio for 2018. United States US: Incidence of HIV: per 1,000 Uninfected Population data is updated yearly, averaging 0.120 Ratio from Dec 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 0.130 Ratio in 2012 and a record low of 0.110 Ratio in 2019. United States US: Incidence of HIV: per 1,000 Uninfected Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Number of new HIV infections among uninfected populations expressed per 1,000 uninfected population in the year before the period.;UNAIDS estimates.;Weighted average;This is the Sustainable Development Goal indicator 3.3.1 [https://unstats.un.org/sdgs/metadata/].

United States US: Incidence of HIV: % of Uninfected Population Aged 15-49 data was reported at 0.020 % in 2014. This stayed constant from the previous number of 0.020 % for 2013. United States US: Incidence of HIV: % of Uninfected Population Aged 15-49 data is updated yearly, averaging 0.030 % from Dec 2008 (Median) to 2014, with 7 observations. The data reached an all-time high of 0.030 % in 2012 and a record low of 0.020 % in 2014. United States US: Incidence of HIV: % of Uninfected 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. Number of new HIV infections among uninfected populations ages 15-49 expressed per 100 uninfected population in the year before the period.; ; UNAIDS estimates.; Weighted Average;

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

aCases of HIV infection newly diagnosed HIV infection in Australia in 2010 including cases previously diagnosed overseas. All new diagnoses inAustralia in 2010, reported by 31 March 2012. All new diagnoses includes cases previously diagnosed overseas, some of whom have received treatment for HIV infection. The heterosexual contact category includes 53M, 80F, total 133 cases from high prevalence countries and 42M, 12F, total 54 cases whose exposure was attributed to heterosexual contact with a partner from a high prevalence country.bThis dataset is not nationally representative; it includes data from 4 of the 13 provinces and territories and one of these jurisdictions was incomplete (nominal cases only). Across all 13 P/Ts, a total of 2,358 HIV cases were reported in 2010.cHIV surveillance includes the whole country except Sardegna region (coverage 97.8%). Data reported by 31 December 2010.dData Source: New HIV Diagnoses Information System (SINIVIH in Spanish). In 2011, SINIVIH was implemented in 17 out of 19 Autonomous Region, and coverage was 71% of the total Spanish population. While information on first CD4 count after diagnosis is available in all Regions, information on DATE of CD4 count determination is currently available only in seven (Aragon, Asturias, Canary Islands, Castile-Leon, Madrid, Murcia and Navarre). This seven regions provided 1519 (52.2%) of the total 2907 new HIV diagnoses notified in 2010. Information on date of CD4 count was missing in 248 (16%) of the 1519 new HIV diagnoses. Information on viral load determination within 3 months after HIV diagnosis is not collected.eIncludes cases of HIV infection diagnosed in 14 jurisdictions of the United States and reported by 31 December 2011. Estimated numbers resulted from statistical adjustment that accounted for missing risk-factor information, but not for reporting delays and incomplete reporting. Age group 10–19 includes 13–19 only.fMSM, men who have sex with men; IDU, injection drug use.

The National Family Health Surveys (NFHS) programme, initiated in the early 1990s, has emerged as a nationally important source of data on population, health, and nutrition for India and its states. The 2005-06 National Family Health Survey (NFHS-3), the third in the series of these national surveys, was preceded by NFHS-1 in 1992-93 and NFHS-2 in 1998-99. Like NFHS-1 and NFHS-2, NFHS-3 was designed to provide estimates of important indicators on family welfare, maternal and child health, and nutrition. In addition, NFHS-3 provides information on several new and emerging issues, including family life education, safe injections, perinatal mortality, adolescent reproductive health, high-risk sexual behaviour, tuberculosis, and malaria. Further, unlike the earlier surveys in which only ever-married women age 15-49 were eligible for individual interviews, NFHS-3 interviewed all women age 15-49 and all men age 15-54. Information on nutritional status, including the prevalence of anaemia, is provided in NFHS3 for women age 15-49, men age 15-54, and young children. A special feature of NFHS-3 is the inclusion of testing of the adult population for HIV. NFHS-3 is the first nationwide community-based survey in India to provide an estimate of HIV prevalence in the general population. Specifically, NFHS-3 provides estimates of HIV prevalence among women age 15-49 and men age 15-54 for all of India, and separately for Uttar Pradesh and for Andhra Pradesh, Karnataka, Maharashtra, Manipur, and Tamil Nadu, five out of the six states classified by the National AIDS Control Organization (NACO) as high HIV prevalence states. No estimate of HIV prevalence is being provided for Nagaland, the sixth high HIV prevalence state, due to strong local opposition to the collection of blood samples. NFHS-3 covered all 29 states in India, which comprise more than 99 percent of India's population. NFHS-3 is designed to provide estimates of key indicators for India as a whole and, with the exception of HIV prevalence, for all 29 states by urban-rural residence. Additionally, NFHS-3 provides estimates for the slum and non-slum populations of eight cities, namely Chennai, Delhi, Hyderabad, Indore, Kolkata, Meerut, Mumbai, and Nagpur. NFHS-3 was conducted under the stewardship of the Ministry of Health and Family Welfare (MOHFW), Government of India, and is the result of the collaborative efforts of a large number of organizations. The International Institute for Population Sciences (IIPS), Mumbai, was designated by MOHFW as the nodal agency for the project. Funding for NFHS-3 was provided by the United States Agency for International Development (USAID), DFID, the Bill and Melinda Gates Foundation, UNICEF, UNFPA, and MOHFW. Macro International, USA, provided technical assistance at all stages of the NFHS-3 project. NACO and the National AIDS Research Institute (NARI) provided technical assistance for the HIV component of NFHS-3. Eighteen Research Organizations, including six Population Research Centres, shouldered the responsibility of conducting the survey in the different states of India and producing electronic data files. The survey used a uniform sample design, questionnaires (translated into 18 Indian languages), field procedures, and procedures for biomarker measurements throughout the country to facilitate comparability across the states and to ensure the highest possible data quality. The contents of the questionnaires were decided through an extensive collaborative process in early 2005. Based on provisional data, two national-level fact sheets and 29 state fact sheets that provide estimates of more than 50 key indicators of population, health, family welfare, and nutrition have already been released. The basic objective of releasing fact sheets within a very short period after the completion of data collection was to provide immediate feedback to planners and programme managers on key process indicators.

United States US: Children: 0-14 Living with HIV data was reported at 2,500.000 Person in 2019. This records a decrease from the previous number of 2,800.000 Person for 2018. United States US: Children: 0-14 Living with HIV data is updated yearly, averaging 3,700.000 Person from Dec 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 4,700.000 Person in 2010 and a record low of 2,500.000 Person in 2019. United States US: Children: 0-14 Living with HIV data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Children living with HIV refers to the number of children ages 0-14 who are infected with HIV.;UNAIDS estimates.;;

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.

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.

Context

HealthStats provides key health, nutrition and population statistics gathered from a variety of international sources. Themes include population dynamics, nutrition, reproductive health, health financing, medical resources and usage, immunization, infectious diseases, HIV/AIDS, DALY, population projections and lending. HealthStats also includes health, nutrition and population statistics by wealth quintiles.

Content

This dataset includes 345 indicators, such as immunization rates, malnutrition prevalence, and vitamin A supplementation rates across 263 countries around the world. Data was collected on a yearly basis from 1960-2016.

Inspiration

• In your opinion, what are some of the more surprising indicators? Are there any you would consider adding?
• Is there a relationship between condom use and rates of children born with HIV? How do these rates compare over time?
• Which countries have the highest consumption of iodized salt? Has this indicator changed over time, and if so, in which countries? Are there any other indicators that seem to correlate with this one?

Acknowledgements

Data was acquired from the World Bank, and can be accessed in multiple formats here.

All figures are given as % (95% CI) unless noted. There were 61 counties in the top decile and 551 in the remaining deciles.

United States US: Newly Infected with HIV: Children: Aged 0-14 data was reported at 200.000 Number in 2019. This stayed constant from the previous number of 200.000 Number for 2018. United States US: Newly Infected with HIV: Children: Aged 0-14 data is updated yearly, averaging 200.000 Number from Dec 2010 (Median) to 2019, with 10 observations. The data reached an all-time high of 500.000 Number in 2012 and a record low of 200.000 Number in 2019. United States US: Newly Infected with HIV: Children: Aged 0-14 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Number of children (ages 0-14) newly infected with HIV.;UNAIDS estimates.;;This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].

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.

Cervical Cancer Risk Factors for Biopsy: This Dataset is Obtained from UCI Repository and kindly acknowledged! This file contains a List of Risk Factors for Cervical Cancer leading to a Biopsy Examination! About 11,000 new cases of invasive cervical cancer are diagnosed each year in the U.S. However, the number of new cervical cancer cases has been declining steadily over the past decades. Although it is the most preventable type of cancer, each year cervical cancer kills about 4,000 women in the U.S. and about 300,000 women worldwide. In the United States, cervical cancer mortality rates plunged by 74% from 1955 - 1992 thanks to increased screening and early detection with the Pap test. AGE Fifty percent of cervical cancer diagnoses occur in women ages 35 - 54, and about 20% occur in women over 65 years of age. The median age of diagnosis is 48 years. About 15% of women develop cervical cancer between the ages of 20 - 30. Cervical cancer is extremely rare in women younger than age 20. However, many young women become infected with multiple types of human papilloma virus, which then can increase their risk of getting cervical cancer in the future. Young women with early abnormal changes who do not have regular examinations are at high risk for localized cancer by the time they are age 40, and for invasive cancer by age 50. SOCIOECONOMIC AND ETHNIC FACTORS Although the rate of cervical cancer has declined among both Caucasian and African-American women over the past decades, it remains much more prevalent in African-Americans -- whose death rates are twice as high as Caucasian women. Hispanic American women have more than twice the risk of invasive cervical cancer as Caucasian women, also due to a lower rate of screening. These differences, however, are almost certainly due to social and economic differences. Numerous studies report that high poverty levels are linked with low screening rates. In addition, lack of health insurance, limited transportation, and language difficulties hinder a poor woman’s access to screening services. HIGH SEXUAL ACTIVITY Human papilloma virus (HPV) is the main risk factor for cervical cancer. In adults, the most important risk factor for HPV is sexual activity with an infected person. Women most at risk for cervical cancer are those with a history of multiple sexual partners, sexual intercourse at age 17 years or younger, or both. A woman who has never been sexually active has a very low risk for developing cervical cancer. Sexual activity with multiple partners increases the likelihood of many other sexually transmitted infections (chlamydia, gonorrhea, syphilis).Studies have found an association between chlamydia and cervical cancer risk, including the possibility that chlamydia may prolong HPV infection. FAMILY HISTORY Women have a higher risk of cervical cancer if they have a first-degree relative (mother, sister) who has had cervical cancer. USE OF ORAL CONTRACEPTIVES Studies have reported a strong association between cervical cancer and long-term use of oral contraception (OC). Women who take birth control pills for more than 5 - 10 years appear to have a much higher risk HPV infection (up to four times higher) than those who do not use OCs. (Women taking OCs for fewer than 5 years do not have a significantly higher risk.) The reasons for this risk from OC use are not entirely clear. Women who use OCs may be less likely to use a diaphragm, condoms, or other methods that offer some protection against sexual transmitted diseases, including HPV. Some research also suggests that the hormones in OCs might help the virus enter the genetic material of cervical cells. HAVING MANY CHILDREN Studies indicate that having many children increases the risk for developing cervical cancer, particularly in women infected with HPV. SMOKING Smoking is associated with a higher risk for precancerous changes (dysplasia) in the cervix and for progression to invasive cervical cancer, especially for women infected with HPV. IMMUNOSUPPRESSION Women with weak immune systems, (such as those with HIV / AIDS), are more susceptible to acquiring HPV. Immunocompromised patients are also at higher risk for having cervical precancer develop rapidly into invasive cancer. DIETHYLSTILBESTROL (DES) From 1938 - 1971, diethylstilbestrol (DES), an estrogen-related drug, was widely prescribed to pregnant women to help prevent miscarriages. The daughters of these women face a higher risk for cervical cancer. DES is no longer prsecribed.

The 2004 Malawi Demographic and Health Survey (MDHS) is a nationally representative survey of 11,698 women age 1549 and 3,261 men age 15-54. The main purpose of the 2004 MDHS is to provide policymakers and programme managers with detailed information on fertility, family planning, childhood and adult mortality, maternal and child health, as well as knowledge of and attitudes related to HIV/AIDS and other sexually transmitted infections (STIs). The 2004 MDHS is designed to provide data to monitor the population and health situation in Malawi as a followup of the 1992 and 2000 MDHS surveys, and the 1996 Malawi Knowledge, Attitudes, and Practices in Health Survey. New features of the 2004 MDHS include the collection of information on use of mosquito nets, domestic violence, anaemia testing of women and children under 5, and HIV testing of adults. The 2004 MDHS survey was implemented by the National Statistical Office (NSO). The Ministry of Health and Population, the National AIDS Commission (NAC), the National Economic Council, and the Ministry of Gender contributed to the development of the questionnaires for the survey. Most of the funds for the local costs of the survey were provided by multiple donors through the NAC. The United States Agency for International Development (USAID) provided additional funds for the technical assistance through ORC Macro. The Department for International Development (DfID) of the British Government, the United Nations Children's Fund (UNICEF), and the United Nations Population Fund (UNFPA) also provided funds for the survey. The Centers of Disease Control and Prevention provided technical assistance in HIV testing. The survey used a two-stage sample based on the 1998 Census of Population and Housing and was designed to produce estimates for key indicators for ten large districts in addition to estimates for national, regional, and urban-rural domains. Fieldwork for the 2004 MDHS was carried out by 22 mobile interviewing teams. Data collection commenced on 4 October 2004 and was completed on 31 January 2005. The principal aim of the 2004 MDHS project was to provide up-to-date information on fertility and childhood mortality levels, nuptiality, fertility preferences, awareness and use of family planning methods, use of maternal and child health services, and knowledge and behaviours related to HIV/AIDS and other sexually transmitted infections. It was designed as a follow-on to the 2000 MDHS survey, a national-level survey of similar scope. The 2004 MDHS survey, unlike the 2000 MDHS, collected blood samples which were later tested for HIV in order to estimate HIV prevalence in Malawi. In broad terms, the 2004 MDHS survey aimed to: Assess trends in Malawi's demographic indicators, principally fertility and mortality Assist in the monitoring and evaluation of Malawi's health, population, and nutrition programmes Advance survey methodology in Malawi and contribute to national and international databases Provide national-level estimates of HIV prevalence for women age 15-49 and men age 15-54. In more specific terms, the 2004 MDHS survey was designed to: Provide data on the family planning and fertility behaviour of the Malawian population and thereby enable policymakers to evaluate and enhance family planning initiatives in the country Measure changes in fertility and contraceptive prevalence and analyse the factors that affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and important social and economic factors Examine basic indicators of maternal and child health and welfare in Malawi, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, and use of immunisation services. Particular emphasis was placed on malaria programmes, including malaria prevention activities and treatment of episodes of fever. Provide levels and patterns of knowledge and behaviour related to the prevention of HIV/AIDS and other sexually transmitted infections Provide national estimates of HIV prevalence Measure the level of infant and adult mortality including maternal mortality at the national level Assess the status of women in the country. MAIN FINDINGS Fertility Fertility Levels and Trends. While there has been a significant decline in fertility in the past two decades from 7.6 children in the early 1980s to 6.0 children per woman in the early 2000s, compared with selected countries in Eastern and Southern Africa, such as Zambia, Tanzania, Mozambique, Kenya, and Uganda, the total fertility rate (TFR) in Malawi is high, lower only than Uganda (6.9). Family planning Knowledge of Contraception. Knowledge of family planning is nearly universal, with 97 percent of women age 15-49 and 97 percent of men age 15-54 knowing at least one modern method of family planning. The most widely known modern methods of contraception among all women are injectables (93 percent), the pill and male condom (90 percent each), and female sterilisation (83 percent). Maternal health Antenatal Care. There has been little change in the coverage of antenatal care (ANC) from a medical professional since 2000 (93 percent in 2004 compared with 91 percent in 2000). Most women receive ANC from a nurse or a midwife (82 percent), although 10 percent go to a doctor or a clinical officer. A small proportion (2 percent) receives ANC from a traditional birth attendant, and 5 percent do not receive any ANC. Only 8 percent of women initiated ANC before the fourth month of pregnancy, a marginal increase from 7 percent in the 2000 MDHS. Adult and Maternal Mortality. Comparison of data from the 2000 and 2004 MDHS surveys indicates that mortality for both women and men has remained at the same levels since 1997 (11-12 deaths per 1,000). Child health Childhood Mortality. Data from the 2004 MDHS show that for the 2000-2004 period, the infant mortality rate is 76 per 1,000 live births, child mortality is 62 per 1,000, and the under-five mortality rate is 133 per 1,000 live births. Nutrition Breastfeeding Practices. Breastfeeding is nearly universal in Malawi. Ninety-eight percent of children are breastfed for some period of time. The median duration of breastfeeding in Malawi in 2004 is 23.2 months, one month shorter than in 2000. HIV/AIDS Awareness of AIDS. Knowledge of AIDS among women and men in Malawi is almost universal. This is true across age group, urban-rural residence, marital status, wealth index, and education. Nearly half of women and six in ten men can identify the two most common misconceptions about the transmission of HIV-HIV can be transmitted by mosquito bites, and HIV can be transmitted by supernatural means-and know that a healthy-looking person can have the AIDS virus.

United States US: Newly Infected with HIV: Adults: Aged 15-24 data was reported at 5,600.000 Number in 2021. This records a decrease from the previous number of 5,900.000 Number for 2020. United States US: Newly Infected with HIV: Adults: Aged 15-24 data is updated yearly, averaging 7,200.000 Number from Dec 2010 (Median) to 2021, with 12 observations. The data reached an all-time high of 10,000.000 Number in 2010 and a record low of 5,600.000 Number in 2021. United States US: Newly Infected with HIV: Adults: Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Number of young people (ages 15-24) newly infected with HIV.;UNAIDS estimates.;;This indicator is related to Sustainable Development Goal 3.3.1 [https://unstats.un.org/sdgs/metadata/].

In late 1985, the National Institute of Justice (NIJ) began its first study of Acquired Immune Deficiency Syndrome (AIDS) in correctional facilities. The objective of the study was to report on the incidence and institutional management of AIDS within the federal and state prison systems, as well as in the nation's largest jails. The study was conducted annually from 1985-1990 and biannually thereafter. This collection contains data collected in 1985-1990 and 1992 via a mail questionnaire. Questionnaires were sent each year to the correctional departments of all 50 states, the federal prison system, and 33 to 37 large city and county jail systems. In addition, in 1987-1990, Canadian prison systems were surveyed. A different questionnaire was used in each of the seven years of data collection. However, each questionnaire addressed the same major topics: inmate population, numbers of inmate cases of AIDS and AIDS-Related Complex (ARC), aggregate results of Human Immunodeficiency Virus (HIV) antibody testing programs, and major policies regarding AIDS (training and education, testing, medical and psycho-social services, housing, precautionary measures, confidentiality), as well as associated legal and cost issues. The 1992 questionnaire collected additional information on the prevalence of and policies toward inmates with tuberculosis.

Abstract

The health and demography of the South African population has been undergoing substantial changes as a result of the rapidly progressing HIV epidemic. Researchers at the University of KwaZulu-Natal and the South African Medical Research Council established The Africa Health Research Studies in 1997 funded by a core grant from The Wellcome Trust, UK. Given the urgent need for high quality longitudinal data with which to monitor these changes, and with which to evaluate interventions to mitigate impact, a demographic surveillance system (DSS) was established in a rural South African population facing a rapid and severe HIV epidemic. The DSS, referred to as the Africa Health Research Institute Demographic Information System (ACDIS), started in 2000.

ACDIS was established to ‘describe the demographic, social and health impact of the HIV epidemic in a population going through the health transition’ and to monitor the impact of intervention strategies on the epidemic. South Africa’s political and economic history has resulted in highly mobile urban and rural populations, coupled with complex, fluid households. In order to successfully monitor the epidemic, it was necessary to collect longitudinal demographic data (e.g. mortality, fertility, migration) on the population and to mirror this complex social reality within the design of the demographic information system. To this end, three primary subjects are observed longitudinally in ACDIS: physical structures (e.g. homesteads, clinics and schools), households and individuals. The information about these subjects, and all related information, is stored in a single MSSQL Server database, in a truly longitudinal way—i.e. not as a series of cross-sections.

The surveillance area is located near the market town of Mtubatuba in the Umkanyakude district of KwaZulu-Natal. The area is 438 square kilometers in size and includes a population of approximately 85 000 people who are members of approximately 11 000 households. The population is almost exclusively Zulu-speaking. The area is typical of many rural areas of South Africa in that while predominantly rural, it contains an urban township and informal peri-urban settlements. The area is characterized by large variations in population densities (20–3000 people/km2). In the rural areas, homesteads are scattered rather than grouped. Most households are multi-generational and range with an average size of 7.9 (SD:4.7) members. Despite being a predominantly rural area, the principle source of income for most households is waged employment and state pensions rather than agriculture. In 2006, approximately 77% of households in the surveillance area had access to piped water and toilet facilities.

To fulfil the eligibility criteria for the ACDIS cohort, individuals must be a member of a household within the surveillance area but not necessarily resident within it. Crucially, this means that ACDIS collects information on resident and non-resident members of households and makes a distinction between membership (self-defined on the basis of links to other household members) and residency (residing at a physical structure within the surveillance area at a particular point in time). Individuals can be members of more than one household at any point in time (e.g. polygamously married men whose wives maintain separate households). As of June 2006, there were 85 855 people under surveillance of whom 33% were not resident within the surveillance area. Obtaining information on non-resident members is vital for a number of reasons. Most importantly, understanding patterns of HIV transmission within rural areas requires knowledge about patterns of circulation and about sexual contacts between residents and their non-resident partners. To be consistent with similar datasets from other INDEPTH Member centres, this data set contains data from resident members only.

During data collection, households are visited by fieldworkers and information supplied by a single key informant. All births, deaths and migrations of household members are recorded. If household members have moved internally within the surveillance area, such moves are reconciled and the internal migrant retains the original identfier associated with him/her.

Geographic coverage

Demographic surveillance area situated in the south-east portion of the uMkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the South by the Umfolozi river, on the East by the N2 highway (except form portions where the Kwamsane township strandles the highway) and in the North by the Inyalazi river for portions of the boundary. The area is 438 square kilometers.

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants are defined by intention to become resident, but actual residence episodes of less than 180 days are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than 180 days are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever resident during the study period (1 Jan 2000 to 31 Dec 2015).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 1 to 37 of demographic surveillance data covering the period from 1 Jan 2000 to 31 December 2015. Two rounds of data collection took place annually except in 2002 when three surveillance rounds were conducted. From 1 Jan 2015 onwards there are three surveillance rounds per annum.

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance area.

Reponse units (households) by year: Year Households 2000 11856
2001 12321
2002 12981
2003 12165
2004 11841
2005 11312
2006 12065
2007 12165
2008 11790
2009 12145
2010 12485
2011 12455
2012 12087 2013 11988 2014 11778 2015 11938

In 2006 the number of response units increased due to the addition of a new village into the demographic surveillance area.

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Bounded structure registration (BSR) or update (BSU) form: - Used to register characteristics of the BS - Updates characteristics of the BS - Information as at previous round is preprinted

Household registration (HHR) or update (HHU) form: - Used to register characteristics of the HH - Used to update information about the composition of the household - Information preprinted of composition and all registered households as at previous

Household Membership Registration (HMR) or update (HMU): - Used to link individuals to households - Used to update information about the household memberships and member status observations - Information preprinted of member status observations as at previous

Individual registration form (IDR): - Used to uniquely identify each individual - Mainly to ensure members with multiple household memberships are appropriately captured

Migration notification form (MGN): - Used to record change in the BS of residency of individuals or households _ Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON): - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH

Death notification form (DTN): - Records all deaths that have recently occurred - Iincludes information about time, place, circumstances and possible cause of death

Cleaning operations

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

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

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

The Malawi Longitudinal Study of Families and Health (MLSFH) [previous title: Malawi Diffusion and Ideational Change Project (MDICP)] is one of very few long- standing longitudinal cohort studies in a poor Sub-Saharan African (SSA) context. It provides a rare record of more than a decade of demographic, socioeconomic, and health conditions in one of the world's poorest countries. The MLSFH cohorts were selected to represent the rural population of Malawi, where the vast majority of Malawians live in conditions that are similar to those in the rural areas of other countries with high HIV prevalence: health conditions are poor, health facilities and schools are over-burdened and under-staffed, standards of living are low and nutritional needs of adults, children and the elderly are often not met. With 7 major data collection rounds between 1998 and 2012 for up to 4,000 individuals, as well as ancillary surveys and qualitative studies, the MLSFH has been a premier dataset for research on health, family dynamics, social networks, and HIV infection risks in a rural SSA context. Providing public-use data on the socioeconomic context, demographics and health of individuals and their families in Malawi over more than a decade, the MLSFH has been the basis of more than 150 publications and working papers submitted for publication. Importantly, the MLSFH has also informed health policy discussions in Malawi and elsewhere in SSA. The MLSFH/MDICP was originally developed as a sister project of the Kenya Diffusion and Ideational Change Project (KDICP), but with a larger sample and greater geographical dispersion. Both the KDICP and the MLSFH/MDICP aimed to examine the role of social interactions in changing demographic attitudes and behavior. The first two waves of the MLSFH data collected in 1998 and 2001 are archived and available for download at ICPSR-DSDR. The first two waves focused on two key empirical questions: the roles of social interactions in (1) the acceptance (or rejection) of modern contraceptive methods and of smaller ideal family size and (2) the diffusion of knowledge of AIDS symptoms and transmission mechanisms and the evaluation of acceptable strategies of protection against AIDS. More information and data for all waves of the MLSFH study can be found on the MLSFH project Web site. The MLSFH Data Web site contains instructions on how individuals can currently obtain the data (6 waves, 1998-2010). The MLSFH Cohort Profile is available as a University of Pennsylvania Population Studies Center (PSC) Working Paper. This cohort profile is the main documentation for the general study design, sampling framework, etc., and it summarizes some key findings as well.