Population effects on individual systolic blood pressure: A multilevel analysis of the World Health Organization MONICA project

Individuals from the same population share a number of contextual circumstances that may condition a common level of blood pressure over and above individual characteristics. Understanding this population effect is relevant for both etiologic research and prevention strategies. Using multilevel regression analyses, the authors quantified the extent to which individual differences in systolic blood pressure (SBP) could be attributed to the population level. They also investigated possible cross-level interactions between the population in which a person lived and pharmacological (antihypertensive medication) and nonpharmacological (body mass index) effects on individual SBP. They analyzed data on 23,796 men and 24,986 women aged 35-64 years from 39 worldwide Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) study populations participating in the final survey of this World Health Organization project (1989-1997). SBP was positively associated with low educational achievement, high body mass index, and use of antihypertensive medication and, for women, was negatively associated with smoking. About 7-8% of all SBP differences between subjects were attributed to the population level. However, this population effect was particularly strong (i.e., 20%) in antihypertensive medication users and overweight women. This empirical evidence of a population effect on individual SBP emphasizes the importance of developing population-wide strategies to reduce individual risk of hypertension.

Violence against young Australian women and association with reproductive events: a cross-sectional analysis of a national population sample

Objective: This study aimed to investigate associations between violence and younger women's reproductive events using Survey 1 (1996) data of the Younger cohort of the Australian Longitudinal Study of Women's Health (ALSWH). Methods: Multinomial regression, using composite variables for both violence and reproductive events, adjusting for socioeconomic variables and weighted for rural and remote areas. Results: 23.8% of 14,784 women aged 18 to 23 years reported violence; 12.6% reported non-partner violence in the previous year; and 11.2% reported ever having had a violent relationship with a partner. Of the latter group, 43% (4.8% overall) also reported violence in the past year. Compared with women reporting no violence, women reporting partner but not recent violence (OR 2.55, 95% Cl 2.10-3.09) or partner and recent violence (OR 3.96, 95% Cl 3.18-4.93) were significantly more likely to have had one or more pregnancies. Conversely, having had a pregnancy (2,561) was associated with an 80% increase in prevalence of any violence and a 230% increase in partner violence. Among women who had a pregnancy, having had a miscarriage or termination was associated with violence. Partner and recent violence is strongly associated with having had a miscarriage, whether alone (OR = 2.85, 95% Cl 1.74-4.66), with a termination (OR = 4.60, 2.26-9.35), or with birth, miscarriage and a termination (OR 4.12, 1.89-9.00). Conclusions and implications: Violence among young women of childbearing age is a factor for which doctors should be vigilant, well-trained and supported to identify and manage effectively.

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.

Cost-effectiveness analysis of a kidney and cardiovascular disease treatment program in an Australian aboriginal population

The objective of the study was to assess, from a health service perspective, whether a systematic program to modify kidney and cardiovascular disease reduced the costs of treating end-stage kidney failure. The participants in the study were 1,800 aboriginal adults with hypertension, diabetes with microalbuminuria or overt albuminuria, and overt albuminuria, living on two islands in the Northern Territory of Australia during 1995 to 2000. Perindopril was the primary treatment agent, and other medications were also used to control blood pressure. Control of glucose and lipid levels were attempted, and health education was offered. Evaluation of program resource use and costs for follow-up periods was done at 3 and 4.7 years. On an intention-to-treat basis, the number of dialysis starts and dialysis-years avoided were estimated by comparing the fate of the treatment group with that of historical control subjects, matched for disease severity, who were followed in the before the treatment program began. For the first three years, an estimated 11.6 person-years of dialysis were avoided, and over 4.7 years, 27.7 person-years of dialysis were avoided. The net cost of the program was $1,210 more per person per year than status quo care, and dialyses avoided gave net savings of $1.0 million at 3 years and $3.4 million at 4.6 years. The treatment program provided significant health benefit and impressive cost savings in dialysis avoided. (C) 2005 by the National Kidney Foundation, Inc.

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.

On parameter estimation in population models

We describe methods for estimating the parameters of Markovian population processes in continuous time, thus increasing their utility in modelling real biological systems. A general approach, applicable to any finite-state continuous-time Markovian model, is presented, and this is specialised to a computationally more efficient method applicable to a class of models called density-dependent Markov population processes. We illustrate the versatility of both approaches by estimating the parameters of the stochastic SIS logistic model from simulated data. This model is also fitted to data from a population of Bay checkerspot butterfly (Euphydryas editha bayensis), allowing us to assess the viability of this population. (c) 2006 Elsevier Inc. All rights reserved.

Accounting for management costs in sensitivity analyses of matrix population models

Traditional sensitivity and elasticity analyses of matrix population models have been used to p inform management decisions, but they ignore the economic costs of manipulating vital rates. For exam le, the growth rate of a population is often most sensitive to changes in adult survival rate, but this does not mean that increasing that rate is the best option for managing the population because it may be much more expensive than other options. To explore how managers should optimize their manipulation of vital rates, we incorporated the cost of changing those rates into matrix population models. We derived analytic expressions for locations in parameter space where managers should shift between management of fecundity and survival, for the balance between fecundity and survival management at those boundaries, and for the allocation of management resources to sustain that optimal balance. For simple matrices, the optimal budget allocation can often be expressed as simple functions of vital rates and the relative costs of changing them. We applied our method to management of the Helmeted Honeyeater (Lichenostomus melanops cassidix; an endangered Australian bird) and the koala (Phascolarctos cinereus) as examples. Our method showed that cost-efficient management of the Helmeted Honeyeater should focus on increasing fecundity via nest protection, whereas optimal koala management should focus on manipulating both fecundity and survival simultaneously, These findings are contrary to the cost-negligent recommendations of elasticity analysis, which would suggest focusing on managing survival in both cases. A further investigation of Helmeted Honeyeater management options, based on an individual-based model incorporating density dependence, spatial structure, and environmental stochasticity, confirmed that fecundity management was the most cost-effective strategy. Our results demonstrate that decisions that ignore economic factors will reduce management efficiency.