Historical dataset of population level and growth rate for the Delhi, India metro area from 1950 to 2025.

Census: Population: Delhi: Delhi data was reported at 16,368,899.000 Person in 03-01-2011. This records an increase from the previous number of 12,877,470.000 Person for 03-01-2001. Census: Population: Delhi: Delhi data is updated decadal, averaging 1,898,271.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 16,368,899.000 Person in 03-01-2011 and a record low of 214,115.000 Person in 03-01-1901. Census: Population: Delhi: Delhi data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC026: Census: Population: By Towns and Urban Agglomerations: NCT of Delhi.

BackgroundIn India, acute respiratory infections (ARIs) are a leading cause of mortality in children under 5 years. Mapping the hotspots of ARIs and the associated risk factors can help understand their association at the district level across India.MethodsData on ARIs in children under 5 years and household variables (unclean fuel, improved sanitation, mean maternal BMI, mean household size, mean number of children, median months of breastfeeding the children, percentage of poor households, diarrhea in children, low birth weight, tobacco use, and immunization status of children) were obtained from the National Family Health Survey-4. Surface and ground-monitored PM2.5 and PM10 datasets were collected from the Global Estimates and National Ambient Air Quality Monitoring Programme. Population density and illiteracy data were extracted from the Census of India. The geographic information system was used for mapping, and ARI hotspots were identified using the Getis-Ord Gi* spatial statistic. The quasi-Poisson regression model was used to estimate the association between ARI and household, children, maternal, environmental, and demographic factors.ResultsAcute respiratory infections hotspots were predominantly seen in the north Indian states/UTs of Uttar Pradesh, Bihar, Delhi, Haryana, Punjab, and Chandigarh, and also in the border districts of Uttarakhand, Himachal Pradesh, and Jammu and Kashmir. There is a substantial overlap among PM2.5, PM10, population density, tobacco smoking, and unclean fuel use with hotspots of ARI. The quasi-Poisson regression analysis showed that PM2.5, illiteracy levels, diarrhea in children, and maternal body mass index were associated with ARI.ConclusionTo decrease ARI in children, urgent interventions are required to reduce the levels of PM2.5 and PM10 (major environmental pollutants) in the hotspot districts. Furthermore, improving sanitation, literacy levels, using clean cooking fuel, and curbing indoor smoking may minimize the risk of ARI in children.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for village level demographic analysis within basic applications to support graphical overlays and analysis with other spatial data.

In 2022, the union territory of Delhi had the highest urban population density of over ** thousand persons per square kilometer. While the rural population density was highest in union territory of Puducherry, followed by the state of Bihar.

Census: Population: Delhi: Delhi: Female data was reported at 7,607,894.000 Person in 03-01-2011. This records an increase from the previous number of 5,808,099.000 Person for 03-01-2001. Census: Population: Delhi: Delhi: Female data is updated decadal, averaging 824,862.000 Person from Mar 1901 (Median) to 03-01-2011, with 12 observations. The data reached an all-time high of 7,607,894.000 Person in 03-01-2011 and a record low of 96,243.000 Person in 03-01-1901. Census: Population: Delhi: Delhi: Female data remains active status in CEIC and is reported by Office of the Registrar General & Census Commissioner, India. The data is categorized under India Premium Database’s Demographic – Table IN.GAC026: Census: Population: By Towns and Urban Agglomerations: NCT of Delhi.

This dataset contains de-identified patient-level data used in the study titled “Cachexia in Gynecologic Cancers: The Role of Biomarkers and Cachexia Index.” he study was conducted to evaluate the prevalence of cachexia and the utility of the Cachexia Index (CXI) in women with gynecologic malignancies, particularly in the Indian population.Variables include demographic information, cancer type and stage, treatment details, biochemical markers, anthropometric measurements, and cachexia index components. The data were collected prospectively the All India Institute of Medical Sciences (AIIMS), New Delhi, India, from July 2022 to June 2024.The dataset is intended to support transparency and reproducibility of the findings and is shared in accordance with ethical and privacy guidelines. All patient identifiers have been removed.

*Denominator for the percentage is # influenza positive for that year.€p<0.01 (highly significant for Influenza A (H3N2) in 2009, OR = 1.8, CI – 1.2–2.7)for peri-urban area.Ψp<0.001 (highly significant for pandemic Influenza A(H1N1)pdm09 in 2009, OR = 7.7, CI – 4.2–14) and 2010 (OR = 3.0, CI – 1.6–5.6) for urban areas.

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.

Mean Age = 13.4 years, s.d = 1.1Distribution of study population based on age, sex, gender and religion (n = 1386).

The present study was designed to explore the STR diversity and genomic history of the inhabitants of the most populous subdivision of the country. A set of 24 hypervariable autosomal STRs was used to estimate the genetic diversity within the studied population. A panel of 15 autosomal STRs, which is most common in the previously reported data sets, was used to estimate the genetic diversity between the studied population, and obtained unique relations were reported here. The genetic diversity and polymorphism among 636 individuals of different ethnic groups, residing in Bareilly, Pilibhit, Shahjahanpur, Gorakhpur, Jhansi, and Varanasi regions of Uttar Pradesh, India, was investigated. This investigation was carried out via 24 autosomal STRs. The 24 loci studied showed the highest value of combined power of discrimination (CPD = 1), combined power of exclusion (CPE = 0.99999999985), combined paternity index (CPI = 6.10 × 109) and lowest combined matching probability (CPM = 7.90 × 10−31). The studied population showed genetic closeness with the population of Uttarakhand, the Jats of Delhi,the Jat Sikh (Punjab), and the population of Rajasthan. Among the tested loci, SE33 and Penta E were found to be most useful in terms of the highest discrimination power, lowest matching probability, the highest power of exclusion, and highest polymorphism information content for the Uttar Pradesh population .