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 Aged 15-49 data was reported at 0.220 Ratio in 2018. This stayed constant from the previous number of 0.220 Ratio for 2017. United States US: Incidence of HIV: per 1,000 Uninfected Population Aged 15-49 data is updated yearly, averaging 0.250 Ratio from Dec 1990 (Median) to 2018, with 29 observations. The data reached an all-time high of 0.290 Ratio in 1990 and a record low of 0.220 Ratio in 2018. United States US: Incidence of HIV: per 1,000 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 United States – Table US.World Bank.WDI: Health Statistics. Number of new HIV infections among uninfected populations ages 15-49 expressed per 1,000 uninfected population in the year before the period.; ; UNAIDS estimates.; Weighted average;

These data were reported to the NYC DOHMH by March 31, 2021

This dataset includes data on new diagnoses of HIV and AIDS in NYC for the calendar years 2016 through 2020. Reported cases and case rates (per 100,000 population) are stratified by United Hospital Fund (UHF) neighborhood, sex, and race/ethnicity.

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;

Global Health Outcomes Data

Impact on Mortality Rates and Malnutrition in Countries Around the World

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides comprehensive insights into critical health conditions around the world, such as mortality rate, malnutrition levels, and frequency of preventable diseases. It documents the prevalence of life-threatening diseases like malaria and tuberculosis, and are tracked alongside key health indicators like adult mortality rates, HIV prevalence, physicians per 10,000 people ratio and public health expenditures. Such metrics provide us with an accurate picture of how developed healthcare systems are in certain countries which ultimately leads to improvements in public policy formation and awareness amongst decision-makers. With this data it is possible to observe disparities between different regions of the world which can help inform global strategies for providing equitable care globally. This dataset is a valuable source for researchers interested in understanding global health trends over time or seeking to evaluate regional differences within countries

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How to use the dataset

This dataset provides comprehensive global health outcome data for countries around the world. It includes vital information such as infant mortality rates, child malnutrition rates, adult mortality rates, deaths due to malaria and tuberculosis, HIV prevalence rates, life expectancy at age 60 and public health expenditure. This dataset can be used to gain valuable insight into the challenges faced by different countries in providing a good quality of life for their citizens.

To use this dataset, first identify what questions you need answered and what outcomes you are looking to measure. You may want to look at specific disease-based indicators (e.g. malaria or tuberculosis), health-related indicators (e.g., nutrition), or overall population markers (e.g., life expectancy).

Then decide which data points from the provided fields will help answer your questions and provide the results needed - e.g,. infant mortality rate or HIV prevalence rate - extracting these values from relevant columns like “Infants lacking immunization (% of one-year-olds) Measles 2013” or “HIV prevalence, adult (% ages 15Ð49) 2013” respectively

Next extract other columnwise relevant information - e.g., country name — that could also aid your analysis using tools like Excel or Python's Pandas library; sorting through them based on any metric desired — e..g,, physicians per 10k people — while being mindful that some data points are missing in some cases (denoted by NA).

Finally perform basic analyses with either your own scripting language, like R/Python libraries' numerical functions with accompanying visuals/graphs etc if elucidating trends is desired; drawing meaningful conclusions about overall state of global health outcomes accordingly before making informed decisions thereafter if needed too!

Research Ideas

• Create a world health map to visualize the differences in health outcomes across different countries and regions.
• Develop an AI-based decision support tool that identifies optimal public health policies or interventions based on these metrics for different countries.
• Design a dashboard or web app that displays and updates this data in real-time, to allow users to compare the current state of global health indicators and benchmark them against historical figures

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: health-outcomes-csv-1.csv | Column name | Description | |:-------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------| | Country | The name of the country. (String) ...

Abstract

The 2005 Guyana HIV/AIDS Indicator Survey (GAIS) is the first household-based, comprehensive survey on HIV/AIDS to be carried out in Guyana. The 2005 GAIS was implemented by the Guyana Responsible Parenthood Association (GRPA) for the Ministry of Health (MoH). ORC Macro of Calverton, Maryland provided technical assistance to the project through its contract with the U.S. Agency for International Development (USAID) under the MEASURE DHS program. Funding to cover technical assistance by ORC Macro and for local costs was provided in their entirety by USAID/Washington and USAID/Guyana.

The 2005 GAIS is a nationally representative sample survey of women and men age 15-49 initiated by MoH with the purpose of obtaining national baseline data for indicators on knowledge/awareness, attitudes, and behavior regarding HIV/AIDS. The survey data can be effectively used to calculate valuable indicators of the President’s Emergency Plan for AIDS Relief (PEPFAR), the Joint United Nations Program on HIV/AIDS (UNAIDS), the United Nations General Assembly Special Session (UNGASS), the United Nations Children Fund (UNICEF) Orphan and Vulnerable Children unit (OVC), and the World Health Organization (WHO), among others. The overall goal of the survey was to provide program managers and policymakers involved in HIV/AIDS programs with information needed to monitor and evaluate existing programs; and to effectively plan and implement future interventions, including resource mobilization and allocation, for combating the HIV/AIDS epidemic in Guyana.

Other objectives of the 2005 GAIS include the support of dissemination and utilization of the results in planning, managing and improving family planning and health services in the country; and enhancing the survey capabilities of the institutions involved in order to facilitate the implementation of surveys of this type in the future.

The 2005 GAIS sampled over 3,000 households and completed interviews with 2,425 eligible women and 1,875 eligible men. In addition to the data on HIV/AIDS indicators, data on the characteristics of households and its members, malaria, infant and child mortality, tuberculosis, fertility, and family planning were also collected.

Geographic coverage

National

Analysis unit

• Individuals;
• Households.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the 2005 GAIS is to provide estimates with acceptable precision for important population characteristics such as HIV/AIDS related knowledge, attitudes, and behavior. The population to be covered by the 2005 GAIS was defined as the universe of all women and men age 15-49 in Guyana.

The major domains to be distinguished in the tabulation of important characteristics for the eligible population are: • Guyana as a whole • The urban area and the rural area each as a separate major domain • Georgetown and the remainder urban areas.

Administratively, Guyana is divided into 10 major regions. For census purposes, each region is further subdivided in enumeration districts (EDs). Each ED is classified as either urban or rural. There is a list of EDs that contains the number of households and population for each ED from the 2002 census. The list of EDs is grouped by administrative units as townships. The available demarcated cartographic material for each ED from the last census makes an adequate sample frame for the 2005 GAIS.

The sampling design had two stages with enumeration districts (EDs) as the primary sampling units (PSUs) and households as the secondary sampling units (SSUs). The standard design for the GAIS called for the selection of 120 EDs. Twenty-five households were selected by systematic random sampling from a full list of households from each of the selected enumeration districts for a total of 3,000 households. All women and men 15-49 years of age in the sample households were eligible to be interviewed with the individual questionnaire.

The database for the recently completed 2002 Census was used as a sampling frame to select the sampling units. In the census frame, EDs are grouped by urban-rural location within the ten administrative regions and they are also ordered in each administrative unit in serpentine fashion. Therefore, this stratification and ordering will be also reflected in the 2005 GAIS sample.

Based on response rates from other surveys in Guyana, around 3,000 interviews of women and somewhat fewer of men expected to be completed in the 3,000 households selected.

Several allocation schemes were considered for the sample of clusters for each urban-rural domain. One option was to allocate clusters to urban and rural areas proportionally to the population in the area. According to the census, the urban population represents only 29 percent of the population of the country. In this case, around 35 clusters out of the 120 would have been allocated to the urban area. Options to obtain the best allocation by region were also examined. It should be emphasized that optimality is not guaranteed at the regional level but the power for analysis is increased in the urban area of Georgetown by departing from proportionality. Upon further analysis of the different options, the selection of an equal number of clusters in each major domain (60 urban and 60 rural) was recommended for the 2005 GAIS. As a result of the nonproportionalallocation of the number of EDs for the urban-rural and regional domains, the household sample for the 2005 GAIS is not a self-weighted sample.

The 2005 GAIS sample of households was selected using a stratified two-stage cluster design consisting of 120 clusters. The first stage-units (primary sampling units or PSUs) are the enumeration areas used for the 2002 Population and Housing Census. The number of EDs (clusters) in each domain area was calculated dividing its total allocated number of households by the sample take (25 households for selection per ED). In each major domain, clusters are selected systematically with probability proportional to size.

The sampling procedures are more fully described in "Guyana HIV/AIDS Indicator Survey 2005 - Final Report" pp.135-138.

Mode of data collection

Face-to-face [f2f]

Research instrument

Two types of questionnaires were used in the survey, namely: the Household Questionnaire and the Individual Questionnaire. The contents of these questionnaires were based on model questionnaires developed by the MEASURE DHS program. In consultation with USAID/Guyana, MoH, GRPA, and other government agencies and local organizations, the model questionnaires were modified to reflect issues relevant to HIV/AIDS in Guyana. The questionnaires were finalized around mid-May.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. For each person listed, information was collected on sex, age, education, and relationship to the head of the household. An important purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview.

The Household Questionnaire also collected non-income proxy indicators about the household's dwelling unit, such as the source of water; type of toilet facilities; materials used for the floor, roof and walls of the house; and ownership of various durable goods and land. As part of the Malaria Module, questions were included on ownership and use of mosquito bednets.

The Individual Questionnaire was used to collect information from women and men age 15-49 years and covered the following topics: • Background characteristics (age, education, media exposure, employment, etc.) • Reproductive history (number of births and—for women—a birth history, birth registration, current pregnancy, and current family planning use) • Marriage and sexual activity • Husband’s background • Knowledge about HIV/AIDS and exposure to specific HIV-related mass media programs • Attitudes toward people living with HIV/AIDS • Knowledge and experience with HIV testing • Knowledge and symptoms of other sexually transmitted infections (STIs) • The malaria module and questions on tuberculosis

Cleaning operations

The processing of the GAIS questionnaires began in mid-July 2005, shortly after the beginning of fieldwork and during the first visit of the ORC Macro data processing specialist. Questionnaires for completed clusters (enumeration districts) were periodically submitted to GRPA offices in Georgetown, where they were edited by data processing personnel who had been trained specifically for this task. The concurrent processing of the data—standard for surveys participating in the DHS program—allowed GRPA to produce field-check tables to monitor response rates and other variables, and advise field teams of any problems that were detected during data entry. All data were entered twice, allowing 100 percent verification. Data processing, including data entry, data editing, and tabulations, was done using CSPro, a program developed by ORC Macro, the U.S. Bureau of Census, and SERPRO for processing surveys and censuses. The data entry and editing of the questionnaires was completed during a second visit by the ORC Macro specialist in mid-September. At this time, a clean data set was produced and basic tables with the basic HIV/AIDS indicators were run. The tables included in the current report were completed by the end of November 2005.

Response rate

• From a total of 3,055 households in the sample, 2,800 were occupied. Among these households, interviews were completed in 2,608, for a response rate of 93 percent. • A total of 2,776 eligible women were identified and

BackgroundConventional epidemiological surveillance of infectious diseases is focused on characterization of incident infections and estimation of the number of prevalent infections. Advances in methods for the analysis of the population-level genetic variation of viruses can potentially provide information about donors, not just recipients, of infection. Genetic sequences from many viruses are increasingly abundant, especially HIV, which is routinely sequenced for surveillance of drug resistance mutations. We conducted a phylodynamic analysis of HIV genetic sequence data and surveillance data from a US population of men who have sex with men (MSM) and estimated incidence and transmission rates by stage of infection.Methods and FindingsWe analyzed 662 HIV-1 subtype B sequences collected between October 14, 2004, and February 24, 2012, from MSM in the Detroit metropolitan area, Michigan. These sequences were cross-referenced with a database of 30,200 patients diagnosed with HIV infection in the state of Michigan, which includes clinical information that is informative about the recency of infection at the time of diagnosis. These data were analyzed using recently developed population genetic methods that have enabled the estimation of transmission rates from the population-level genetic diversity of the virus. We found that genetic data are highly informative about HIV donors in ways that standard surveillance data are not. Genetic data are especially informative about the stage of infection of donors at the point of transmission. We estimate that 44.7% (95% CI, 42.2%–46.4%) of transmissions occur during the first year of infection.ConclusionsIn this study, almost half of transmissions occurred within the first year of HIV infection in MSM. Our conclusions may be sensitive to un-modeled intra-host evolutionary dynamics, un-modeled sexual risk behavior, and uncertainty in the stage of infected hosts at the time of sampling. The intensity of transmission during early infection may have significance for public health interventions based on early treatment of newly diagnosed individuals.Please see later in the article for the Editors' Summary

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/].

Note: ARDA – Association of Religious Data Archives; BLS – Bureau of Labor Statistics; CDC – Centers for Disease Control and Prevention; FBI – Federal Bureau of Investigation; SAMSHA N-SSATS – Substance Abuse and Mental Health Services National Survey of Substance Abuse Treatment Services. We used intercensal estimates of population aged 15–64 [66], [67].*US AIDS Mortality Surveillance Data for 1991–2006 received by special data request (2009) from the US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for HIV and TB Prevention.**Estimates of IDUs per 10,000 adult population are estimates of the proportion of the adult population who injected drugs in the prior year.***Gini coefficients are measures of the extent to which distributions of resources within a population would need to change to create equality. Zero represents equality, 1 represents maximum inequality. The household Gini used here presents data on inequality in household incomes.

The data were extracted from CDC WONDER.

This portion of the GapMinder data includes one year of numerous country-level indicators of health, wealth and development for 213 countries.

GapMinder collects data from a handful of sources, including the Institute for Health
Metrics and Evaluation, US Census Bureau’s International Database, United Nations Statistics Division, and the World Bank. Source: https://www.gapminder.org/

Variable Name , Description of Indicator & Sources Unique Identifier: Country

1. incomeperperson : 2010 Gross Domestic Product per capita in constant 2000 US$.The inflation but not the differences in the cost of living between countries has been taken into account. [Main Source : World Bank Work Development Indicators]

2. alcconsumption: 2008 alcohol consumption per adult (age 15+), litres Recorded and estimated average alcohol consumption, adult (15+) percapita consumption in liters pure alcohol [Main Source : WHO]

3. armedforcesrate: Armed forces personnel (% of total labor force) [Main Source : Work Development Indicators]

4. breastcancerper100TH : 2002 breast cancer new cases per 100,000 female Number of new cases of breast cancer in 100,000 female residents during the certain year. [Main Source : ARC (International Agency for Research on Cancer)]

5. co2emissions : 2006 cumulative CO2 emission (metric tons), Total amount of CO2 emission in metric tons since 1751. [*Main Source : CDIAC (Carbon Dioxide Information Analysis Center)] *

6. femaleemployrate : 2007 female employees age 15+ (% of population) Percentage of female population, age above 15, that has been employed during the given year. [ Main Source : International Labour Organization]

7. employrate : 2007 total employees age 15+ (% of population) Percentage of total population, age above 15, that has been employed during the given year. [Main Source : International Labour Organization]

8. HIVrate : 2009 estimated HIV Prevalence % - (Ages 15-49) Estimated number of people living with HIV per 100 population of age group 15-49. [Main Source : UNAIDS online database]

9. Internetuserate: 2010 Internet users (per 100 people) Internet users are people with access to the worldwide network. [Main Source : World Bank]

10. lifeexpectancy : 2011 life expectancy at birth (years) The average number of years a newborn child would live if current mortality patterns were to stay the same. [Main Source : 1) Human Mortality Database, 2) World Population Prospects: , 3) Publications and files by history prof. James C Riley , 4) Human Lifetable Database ]

11. oilperperson : 2010 oil Consumption per capita (tonnes per year and person) [Main Source : BP]

12. polityscore : 2009 Democracy score (Polity) Overall polity score from the Polity IV dataset, calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 the highest. [Main Source : Polity IV Project]

13. relectricperperson : 2008 residential electricity consumption, per person (kWh) . The amount of residential electricity consumption per person during the given year, counted in kilowatt-hours (kWh). [Main Source : International Energy Agency]

14. suicideper100TH : 2005 Suicide, age adjusted, per 100 000 Mortality due to self-inflicted injury, per 100 000 standard population, age adjusted . [Main Source : Combination of time series from WHO Violence and Injury Prevention (VIP) and data from WHO Global Burden of Disease 2002 and 2004.]

15. urbanrate : 2008 urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects) [Main Source : World Bank]

The Online Tuberculosis Information System (OTIS) on CDC WONDER contains information on verified tuberculosis (TB) cases reported to the Centers for Disease Control and Prevention (CDC) by state health departments, the District of Columbia and Puerto Rico since 1993. These data were extracted from the CDC national TB surveillance system. OTIS reports case counts, incidence rates, population counts, percentage of cases that completed therapy within 1 year of diagnosis, and percentage of cases tested for drug susceptibility. Data for 22 variables are included in the data set, including: age groups, race / ethnicity, sex, vital status, year reported, state, metropolitan area, several patient risk factors, directly observed therapy, disease verification criteria and multi-drug resistant TB. Each year these data are updated with an additional year of cases plus revisions to cases reported in previous years. OTIS is produced by the U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control and Prevention (CDC), National Center for HIV/AIDS, viral Hepatitis, STD and TB Prevention (NCHHSTP).

Although HIV caused one of the worst epidemics since the late twentieth century, China and the U.S. has made substantial progress to control the spread of HIV/AIDS. However, the trends of HIV/AIDS incidence remain unclear in both countries. Therefore, this study aimed to highlight the long-term trends of HIV/AIDS incidence by gender in China and the U.S. population. The data were retrieved from the Global Burden of Disease (GBD) database since it would be helpful to assess the impact/role of designed policies in the control of HIV/AIDS incidence in both countries. The age-period-cohort (APC) model and join-point regression analysis were employed to estimate the age-period-cohort effect and the average annual percentage change (AAPC) on HIV incidence. Between 1994 and 2019, we observed an oscillating trend of the age-standardized incidence rate (ASIR) in China and an increasing ASIR trend in the U.S. Despite the period effect in China declined for both genders after peaked in 2004, the age effect in China grew among the young (from 15–19 to 25–29) and the old age groups (from 65–69 to 75–79). Similarly, the cohort effect increased among those born in the early (from 1924–1928 to 1934–1938) and the latest birth groups (from 1979–1983 to 2004–2009). In the case of the U.S., the age effect declined after it peaked in the 25–29 age group. People born in recent birth groups had a higher cohort effect than those born in early groups. In both countries, women were less infected by HIV than men. Therefore, besides effective strategies and awareness essential to protect the young age groups from HIV risk factors, the Chinese government should pay attention to the elderly who lacked family support and were exposed to HIV risk factors.

WHO Health Indicators for Algeria

Healthcare Delivery, Socioeconomic and Demographic Statistics

By Humanitarian Data Exchange [source]

About this dataset

This dataset from the World Health Organization (WHO) contains comprehensive data on various health indicators for Algeria. It covers various topics such as mortality, sustainable development, global health estimates, health systems, malaria and tuberculosis, child and infection diseases, public health and environment, substance use and mental health tobacco injuries and violence HIV/AIDS nutrition urban Health noncommunicable diseases financial protection medical equipment demographic socioeconomic statistics essential health technologies medical equipment insecticide resistance oral Health universal healthcare global observatory for eHealth human resources information systems youth AMR glass noncommunicable diseases mental healthcare workforce neglected tropical diseases AMR GASP ICD sexual reproductive care and many more.

It provides resource descriptions that allow users to access individual indicator metadata as well as detailed coverage on different countries in the world. The dataset also includes methods related to registry interlinked with last updated information from WHO’s data portal and license terms provided under a variety of other sources.

The analysis of this dataset allows us to know more about the state of public healthcare in Algeria which can eventually lead us not only to an improved understanding but also better initiatives that are designed to benefit the wellbeing of citizens across this nation

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How to use the dataset

This dataset contains a wide variety of health-related indicators for Algeria from the World Health Organization’s (WHO) global health portal. This data can be used to gain insights into various socio-economic conditions, health systems, and public policy strategies in Algeria.

Getting Started

Firstly, you should download the dataset from Kaggle and unzip it into a folder of your choice. Then open the .csv file with your favorite spreadsheet or text editor application. After you have opened the dataset file, you will be able to see all of the available categories and indicator variables included in this dataset.

Understanding The Dataset

The columns in this dataset are divided into two categories; GHO (Global Health Observatory) metadata fields and variable fields describing each indicator value. The GHO metadata fields provide contextual information on where each individual healthcare indicator was sourced from including its reference year(s), geographic region/country, data source code/url and publication state code/url among others. These types of fields can be helpful when interpreting more specific results related to an entire given region or country for example. The second category includes variable fields that contain individual healthcare indicators such as mortality rates or access to clean water for example related to a specific region or population group within Algeria as well as their corresponding statistical values such as low & high values collected over a period of time etc.. Additionally it is important to note that columns with ** after them indicate labels which are relevant only if applicable e..g Low**

Best Practices For Analysing Data

When analysing this type of data consider which comparison type(s) would work best given your end goal: absolute comparison between 2+ geographies over same timeframe? Two periods compared comparatively within same geography? Or different measurements all using same base geography (ie one country)? Once you decide what type of analysis makes sense then use applicable filters/areas such as regions , provinces etc & start slicing up datasets according to whatever measure works best until desired outcomes are found e..g filter out by age groups / sex / marital status / ethnicity etc rather than downloading entire table with all stats together thereby simplifying efforts & narrowing down scope greatly improving accuracy along way whilst identifying potential must know trends quickly through visualisations generated Charts / tables when combined often highlight underlying relationships quickly which is key before analysing further Deep diving combined datasets by cross referencing various indices allowing viewers gain even better insights specially when combined structured narrative explanations composed backed up by facts

Research Ideas

• Creating visualizations that show changes in health indicators over tim...

About 40% of the Italian HIV-1 epidemic due to non-B variants is sustained by F1 clade, which circulates at high prevalence in South America and Eastern Europe. Aim of this study was to define clade F1 origin, population dynamics and epidemiological networks through phylogenetic approaches. We analyzed pol sequences of 343 patients carrying F1 subtype stored in the ARCA database from 1998 to 2009. Citizenship of patients was as follows: 72.6% Italians, 9.3% South Americans and 7.3% Rumanians. Heterosexuals, Homo-bisexuals, Intravenous Drug Users accounted for 58.1%, 24.0% and 8.8% of patients, respectively. Phylogenetic analysis indicated that 70% of sequences clustered in 27 transmission networks. Two distinct groups were identified; the first clade, encompassing 56 sequences, included all Rumanian patients. The second group involved the remaining clusters and included 10 South American Homo-bisexuals in 9 distinct clusters. Heterosexual modality of infection was significantly associated with the probability to be detected in transmission networks. Heterosexuals were prevalent either among Italians (67.2%) or Rumanians (50%); by contrast, Homo-bisexuals accounted for 71.4% of South Americans. Among patients with resistant strains the proportion of clustering sequences was 57.1%, involving 14 clusters (51.8%). Resistance in clusters tended to be higher in South Americans (28.6%) compared to Italian (17.7%) and Rumanian patients (14.3%). A striking proportion of epidemiological networks could be identified in heterosexuals carrying F1 subtype residing in Italy. Italian Heterosexual males predominated within epidemiological clusters while foreign patients were mainly Heterosexual Rumanians, both males and females, and South American Homo-bisexuals. Tree topology suggested that F1 variant from South America gave rise to the Italian F1 epidemic through multiple introduction events. The contact tracing also revealed an unexpected burden of resistance in epidemiological clusters underlying the need of public interventions to limit the spread of non-B subtypes and transmitted drug resistance.

Number of cases attributable to heterosexual contact, statistically adjusted to account for reporting delays and missing risk factor information, but not for incomplete reporting.†Per 100,000 heterosexuals.§ Hispanics/Latinos may be of any race.¶ Other race includes American Indian/Alaska Native, Native Hawaiian/Other Pacific Islander, unknown race/ethnicity, and multiple races.* Relative standard error >30% for meta-analysis estimate of the population proportion heterosexual for this group.Note. Data include persons age 13 years and older with a diagnosis of HIV infection regardless of stage of disease at diagnosis. CI = confidence interval

Note. Data include persons with diagnosed HIV infection regardless of stage of disease at diagnosis. HIV diagnosis data were statistically adjusted for missing transmission category, but not for reporting delays or incomplete reporting.MSM, men who reported ever having had sexual contact with other men.aRates are per 100,000 population.Diagnoses of HIV infection among black/African American MSM and non-MSM, by age at diagnosis, 2005–2009—17 areas.

Abbreviations: SE  =  standard error; OR  =  Odds Ratio; CI  =  Confidence Interval; HIV/AIDS  =  Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome; HSV  =  Herpes Simplex Virus; VZV  =  Varicella Zoster Virus; HHV  =  Human Herpesvirus; NOS  =  not otherwise specified.NA  =  not available due to observations ≤10 admissions suppressed in compliance with Agency for Healthcare Research and Quality guidelines.a) 392 admissions (n = 2,364 weighted) with missing death, sex, or hospital location/teaching status were excluded.b) Total deaths were weighted to represent the entire US population using discharge weights provided by the Nationwide Inpatient Sample.c) The logistic regression model excludes the following disease categories: Unspecified, Other viral, Other infectious, Viral NOS, and Toxoplasmosis (n = 26,232; 128,679 weighted); Final unweighted n = 21,972, weighted n = 107,985. Model constant = 0.012 ± 0.003; Model calibration: Hosmer and Lemeshow goodness-of-fit test: χ2  =  1.803, df  =  9, P = 0.063.d) The Charlson Comorbidity Index is a weighted index based on the presence of 17 comorbid conditions. Each comorbid disease is assigned a weight from 1 to 6; the index is the sum of the weights for each comorbid condition and can range from 0 to 33. For the purposes of this study, mild was score  =  0, moderate score  =  1, and severe score ≥ 2.e) Cancer deaths include both metastatic (30%) and nonmetastatic (70%); crude mortality rates are 15.1% and 14.5%, respectively.Mortality of encephalitis hospitalizations, 2000–2010.

aNational AIDS Register include the whole country. Data collected at 12/31/2011.bSpanish AIDS Register which covers the whole country. Information on date of of HIV dianosis is available in 900 (96.8%) of AIDS cases notified to the Register.cNew diagnoses of AIDS, 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.dMSM, men who have sex with men; IDU, injection drug use.

CAPI  =  Computer Assisted Personal Interview; ACASI  =  Audio Computer-Assisted Self Interview.*Spanish GSS interviews began in 2006.†The public dataset used in this analysis is limited to persons aged 20–69 years, and only those 20–59 answered drug use questions.§GSS data are collected every two years.¶Between 1973 and 2002, NSFG data were collected in cycles. Since 2006, NSFG data are continuously collected. This analysis included data from Cycle 6 (2002) and 2006 through 2008.