Data on the Spanish ancestry population available from the 1970 census of population and housing /

Mode of access: Internet.

Evaluation and research program of the U.S. censuses of population and housing, 1960 : accuracy of data on housing characteristics.

Mode of access: Internet.

Evaluation and research program of the U.S. censuses of population and housing, 1960 : accuracy of data on population characteristics as measured by reinterviews.

Mode of access: Internet.

Evaluation and research program of the U.S. censuses of population and housing, 1960 : accuracy of data on population characteristics for subgroups of the total population.

Bibliography: p. 76.

Marshallese community pilot survey, Northwest Arkansas, USA : population and housing data : including comparative data from surveys conducted on Marshallese communities in Hawaii, Guam, and CNMI and RMI 1998 pre-census survey /

Cover title.

Portland area demographic statistical data report : selected counties with significant Indian population.

Chiefly tables.

The American counties; a record of the origin of the names of the 3,072 counties, dates of creation and organization, area, 1960 population, historical data, etc. of the fifty States.

Mode of access: Internet.

Do you know which 1990 products contain data on the older population?

Mode of access: Internet.

Do you know which 1990 products contain data on the Hispanic origin population?

"Issued August 1993"--P. 24.

Do you know which 1990 products contains the data on the American Indian, Eskimo, and Aleut population?

Mode of access: Internet.

New projections of the veteran population : a review of the data.

Cover title.

Gross flow data from the current population survey, 1970-80 /

"PB82-174327."

The use of state and local labor market data from the Current population survey : the New York experience /

Presented at 14th annual meeting of the Association of Public Data Users, Washington, D.C., Oct. 24, 1989.

Analysis of population pharmacokinetic data using NONMEM and WinBUGS

The aim of this report is to describe the use of WinBUGS for two datasets that arise from typical population pharmacokinetic studies. The first dataset relates to gentamicin concentration-time data that arose as part of routine clinical care of 55 neonates. The second dataset incorporated data from 96 patients receiving enoxaparin. Both datasets were originally analyzed by using NONMEM. In the first instance, although NONMEM provided reasonable estimates of the fixed effects parameters it was unable to provide satisfactory estimates of the between-subject variance. In the second instance, the use of NONMEM resulted in the development of a successful model, albeit with limited available information on the between-subject variability of the pharmacokinetic parameters. WinBUGS was used to develop a model for both of these datasets. Model comparison for the enoxaparin dataset was performed by using the posterior distribution of the log-likelihood and a posterior predictive check. The use of WinBUGS supported the same structural models tried in NONMEM. For the gentamicin dataset a one-compartment model with intravenous infusion was developed, and the population parameters including the full between-subject variance-covariance matrix were available. Analysis of the enoxaparin dataset supported a two compartment model as superior to the one-compartment model, based on the posterior predictive check. Again, the full between-subject variance-covariance matrix parameters were available. Fully Bayesian approaches using MCMC methods, via WinBUGS, can offer added value for analysis of population pharmacokinetic data.

Global and regional burden of disease and risk factors, 2001: systematic analysis of population health data

Background Our aim was to calculate the global burden of disease and risk factors for 2001, to examine regional trends from 1990 to 2001, and to provide a starting point for the analysis of the Disease Control Priorities Project (DCPP). Methods We calculated mortality, incidence, prevalence, and disability adjusted life years (DALYs) for 136 diseases and injuries, for seven income/geographic country groups. To assess trends, we re-estimated all-cause mortality for 1990 with the same methods as for 2001. We estimated mortality and disease burden attributable to 19 risk factors. Findings About 56 million people died in 2001. Of these, 10.6 million were children, 99% of whom lived in low-and-middle-income countries. More than half of child deaths in 2001 were attributable to acute respiratory infections, measles, diarrhoea, malaria, and HIV/AIDS. The ten leading diseases for global disease burden were perinatal conditions, lower respiratory infections, ischaemic heart disease, cerebrovascular disease, HIV/AIDS, diarrhoeal diseases, unipolar major depression, malaria, chronic obstructive pulmonary disease, and tuberculosis. There was a 20% reduction in global disease burden per head due to communicable, maternal, perinatal, and nutritional conditions between 1990 and 2001. Almost half the disease burden in low-and-middle-income countries is now from non-communicable diseases (disease burden per head in Sub-Saharan Africa and the low-and-middle-income countries of Europe and Central Asia increased between 1990 and 2001). Undernutrition remains the leading risk factor for health loss. An estimated 45% of global mortality and 36% of global disease burden are attributable to the joint hazardous effects of the 19 risk factors studied. Uncertainty in all-cause mortality estimates ranged from around 1% in high-income countries to 15-20% in Sub-Saharan Africa. Uncertainty was larger for mortality from specific diseases, and for incidence and prevalence of non-fatal outcomes. Interpretation Despite uncertainties about mortality and burden of disease estimates, our findings suggest that substantial gains in health have been achieved in most populations, countered by the HIV/AIDS epidemic in Sub-Saharan Africa and setbacks in adult mortality in countries of the former Soviet Union. our results on major disease, injury, and risk factor causes of loss of health, together with information on the cost-effectiveness of interventions, can assist in accelerating progress towards better health and reducing the persistent differentials in health between poor and rich countries.