A generic model for the assessment of disease epidemiology: the computational basis of DisMod II

Epidemiology as an empirical science has developed sophisticated methods to measure the causes and patterns of disease in populations. Nevertheless, for many diseases in many countries only partial data are available. When the partial data are insufficient, but data collection is not an option, it is possible to supplement the data by exploiting the causal relations between the various variables that describe a disease process. We present a simple generic disease model with incidence, one prevalent state, and case fatality and remission. We derive a set of equations that describes this disease process and allows calculation of the complete epidemiology of a disease given a minimum of three input variables. We give the example of asthma with age-specific prevalence, remission, and mortality as inputs. Outputs are incidence and case fatality, among others. The set of equations is embedded in a software package called 'DisMod II', which is made available to the public domain by the World Health Organization

Estimating summary measures of health: a structured workbook approach

BACKGROUND: Summary measures of health that combine mortality and morbidity into a single indicator are being estimated in the Canadian context for approximately 200 diseases and conditions. To manage the large amount of data and calculations for this many diseases, we have developed a structured workbook system with easy to use tools. We expect this system will be attractive to researchers from other countries or regions of Canada who are interested in estimating the health-adjusted life years (HALYs) lost to premature mortality and year-equivalents lost to reduced functioning, as well as population attributable fractions (PAFs) associated with risk factors. This paper describes the workbook system using cancers as an example, and includes the entire system as a free, downloadable package. METHODS: The workbook system was developed in Excel and runs on a personal computer. It is a database system that stores data on population structure, mortality, incidence, distributions of cases entering a multitude of health states, durations of time spent in health states, preference scores that weight for severity, life table estimates of life expectancies, and risk factor prevalence and relative risks. The tools are Excel files with embedded macro programs. The main tool generates workbooks that estimate HALY, one per disease, by copying data from the database into a pre-defined template. Other tools summarize the HALY results across diseases for easy analysis. RESULTS: The downloadable zip file contains the database files initialized with Canadian data for cancers, the tools, templates and workbooks that estimate PAF and a user guide. The workbooks that estimate HALY are generated from the system at a rate of approximately one minute per disease. The resulting workbooks are self-contained and can be used directly to explore the details of a particular disease. Results can be discounted at different rates through simple parameter modification. CONCLUSION: The structured workbook approach offers researchers an efficient, easy to use, and easy to understand set of tools for estimating HALY and PAF summary measures for their country or region of interest

Estimating the prevalence of breast cancer using a disease model: data problems and trends

BACKGROUND: Health policy and planning depend on quantitative data of disease epidemiology. However, empirical data are often incomplete or are of questionable validity. Disease models describing the relationship between incidence, prevalence and mortality are used to detect data problems or supplement missing data. Because time trends in the data affect their outcome, we compared the extent to which trends and known data problems affected model outcome for breast cancer. METHODS: We calculated breast cancer prevalence from Dutch incidence and mortality data (the Netherlands Cancer Registry and Statistics Netherlands) and compared this to regionally available prevalence data (Eindhoven Cancer Registry, IKZ). Subsequently, we recalculated the model adjusting for 1) limitations of the prevalence data, 2) a trend in incidence, 3) secondary primaries, and 4) excess mortality due to non-breast cancer deaths. RESULTS: There was a large discrepancy between calculated and IKZ prevalence, which could be explained for 60% by the limitations of the prevalence data plus the trend in incidence. Secondary primaries and excess mortality had relatively small effects only (explaining 17% and 6%, respectively), leaving a smaller part of the difference unexplained. CONCLUSION: IPM models can be useful both for checking data inconsistencies and for supplementing incomplete data, but their results should be interpreted with caution. Unknown data problems and trends may affect the outcome and in the absence of additional data, expert opinion is the only available judge

Forecasting Tunisian type 2 diabetes prevalence to 2027: validation of a simple model.

BACKGROUND: Most projections of type 2 diabetes (T2D) prevalence are simply based on demographic change (i.e. ageing). We developed a model to predict future trends in T2D prevalence in Tunisia, explicitly taking into account trends in major risk factors (obesity and smoking). This could improve assessment of policy options for prevention and health service planning. METHODS: The IMPACT T2D model uses a Markov approach to integrate population, obesity and smoking trends to estimate future T2D prevalence. We developed a model for the Tunisian population from 1997 to 2027, and validated the model outputs by comparing with a subsequent T2D prevalence survey conducted in 2005. RESULTS: The model estimated that the prevalence of T2D among Tunisians aged over 25 years was 12.0% in 1997 (95% confidence intervals 9.6%-14.4%), increasing to 15.1% (12.5%-17.4%) in 2005. Between 1997 and 2005, observed prevalence in men increased from 13.5% to 16.1% and in women from 12.9% to 14.1%. The model forecast for a dramatic rise in prevalence by 2027 (26.6% overall, 28.6% in men and 24.7% in women). However, if obesity prevalence declined by 20% in the 10 years from 2013, and if smoking decreased by 20% over 10 years from 2009, a 3.3% reduction in T2D prevalence could be achieved in 2027 (2.5% in men and 4.1% in women). CONCLUSIONS: This innovative model provides a reasonably close estimate of T2D prevalence for Tunisia over the 1997-2027 period. Diabetes burden is now a significant public health challenge. Our model predicts that this burden will increase significantly in the next two decades. Tackling obesity, smoking and other T2D risk factors thus needs urgent action. Tunisian decision makers have therefore defined two strategies: obesity reduction and tobacco control. Responses will be evaluated in future population surveys

Estimating health-adjusted life expectancy conditional on risk factors: results for smoking and obesity

BACKGROUND: Smoking and obesity are risk factors causing a large burden of disease. To help formulate and prioritize among smoking and obesity prevention activities, estimations of health-adjusted life expectancy (HALE) for cohorts that differ solely in their lifestyle (e.g. smoking vs. non smoking) can provide valuable information. Furthermore, in combination with estimates of life expectancy (LE), it can be tested whether prevention of obesity and smoking results in compression of morbidity. METHODS: Using a dynamic population model that calculates the incidence of chronic disease conditional on epidemiological risk factors, we estimated LE and HALE at age 20 for a cohort of smokers with a normal weight (BMI < 25), a cohort of non-smoking obese people (BMI>30) and a cohort of 'healthy living' people (i.e. non smoking with a BMI < 25). Health state valuations for the different cohorts were calculated using the estimated disease prevalence rates in combination with data from the Dutch Burden of Disease study. Health state valuations are multiplied with life years to estimate HALE. Absolute compression of morbidity is defined as a reduction in unhealthy life expectancy (LE-HALE) and relative compression as a reduction in the proportion of life lived in good health (LE-HALE)/LE. RESULTS: Estimates of HALE are highest for a 'healthy living' cohort (54.8 years for men and 55.4 years for women at age 20). Differences in HALE compared to 'healthy living' men at age 20 are 7.8 and 4.6 for respectively smoking and obese men. Differences in HALE compared to 'healthy living' women at age 20 are 6.0 and 4.5 for respectively smoking and obese women. Unhealthy life expectancy is about equal for all cohorts, meaning that successful prevention would not result in absolute compression of morbidity. Sensitivity analyses demonstrate that although estimates of LE and HALE are sensitive to changes in disease epidemiology, differences in LE and HALE between the different cohorts are fairly robust. In most cases, elimination of smoking or obesity does not result in absolute compression of morbidity but slightly increases the part of life lived in good health. CONCLUSION: Differences in HALE between smoking, obese and 'healthy living' cohorts are substantial and similar to differences in LE. However, our results do not indicate that substantial compression of morbidity is to be expected as a result of successful smoking or obesity prevention

DYNAMO-HIA–A Dynamic Modeling Tool for Generic Health Impact Assessments

Currently, no standard tool is publicly available that allows researchers or policy-makers to quantify the impact of policies using epidemiological evidence within the causal framework of Health Impact Assessment (HIA). A standard tool should comply with three technical criteria (real-life population, dynamic projection, explicit risk-factor states) and three usability criteria (modest data requirements, rich model output, generally accessible) to be useful in the applied setting of HIA. With DYNAMO-HIA (Dynamic Modeling for Health Impact Assessment), we introduce such a generic software tool specifically designed to facilitate quantification in the assessment of the health impacts of policies

Calculation of health expectancies with administrative data for North Rhine-Westphalia, a Federal State of Germany, 1999–2005

Pinheiro JP, Krämer A. Calculation of health expectancies with administrative data for North Rhine-Westphalia, a Federal State of Germany, 1999-2005. Population Health Metrics. 2009;7(1):4.OBJECTIVES: The main objectives of this study were to prove the feasibility of health expectancy analyses with regional administrative health statistics and to explore the utility of the calculated health expectancies in describing the health state of the population living in North Rhine-Westphalia, a Federal State of Germany. MATERIALS AND METHODS: Administrative population and mortality data as well as health data on disability and long-term care provided by public services were used to calculate: a) the life expectancy and b) the health expectancies Severe-Disability-Free Life Expectancy (SDFLE) and Long-Term-Care-Free Life Expectancy (LTCFLE) from 1999 to 2005. Calculations were done using the Sullivan method. RESULTS: SDFLE at birth was 69.9 years (males 66.2 and females 73.2 years) in 1999 and it increased to 71.7 years (males 68.6 and females 74.7 years) in 2005. The proportion of the SDFLE on the total life expectancy at birth was 89.8% (males 88.6 and females 90.8%) in 1999 and 90.7% (males 89.8 and females 91.4%) in 2005.LTCFLE at birth was 75.3 years (males 73.1 and females 77.5 years) in 1999 and it increased to 76.6 years (males 74.7 and females 78.6 years) in 2005. The proportion of the LTCFLE on the total life expectancy at birth was 96.8% (males 97.8 and females 96.1%) in 1999 and 96.8% (males 97.8 and females 96.2%) in 2005. DISCUSSION AND CONCLUSION: Both health expectancies indicate an improvement in the quantity as well as in the quality of healthy life for the population living in North Rhine Westphalia and therefore suggest a compression of morbidity from 1999 to 2005. The findings however have several limitations in their sensitivity, since we applied dichotomous valuations to the health states. In addition, the results are restricted to comparisons over time because the morbidity concepts do not allow for comparisons with populations other than the German one. Refined calculations with other summary measures of population health and with health data on other morbidity concepts are therefore reasonable

The Burden of Trachoma in South Sudan: Assessing the Health Losses from a Condition of Graded Severity

Trachoma is an infectious disease that is endemic to the Republic of South Sudan. In the absence of appropriate treatment recurrent re-infection in an individual will lead to progressively severe states of trachoma, eventually leading to the loss of visual acuity and finally blindness. Here we distinguish between three separate states of disease: trachoma with normal vision, trachoma with low vision and trachoma with blindness. The first of these states, trachoma with normal vision, is the least severe and the impact of this state on a population has not been well investigated. Trachoma, even before any loss of vision, comes with a great deal of pain and social consequences, and thus disability. In this study we employ data from South Sudan and estimate the burden caused by trachoma with normal vision for the first time. In doing so, we also reveal the extent of the gaps in our knowledge surrounding the natural history of trachoma and highlight areas of research that require urgent attention

Higher education delays and shortens cognitive impairment. A multistate life table analysis of the US Health and Retirement Study

Improved health may extend or shorten the duration of cognitive impairment by postponing incidence or death. We assess the duration of cognitive impairment in the US Health and Retirement Study (1992–2004) by self reported BMI, smoking and levels of education in men and women and three ethnic groups. We define multistate life tables by the transition rates to cognitive impairment, recovery and death and estimate Cox proportional hazard ratios for the studied determinants. 95% confidence intervals are obtained by bootstrapping. 55 year old white men and women expect to live 25.4 and 30.0 years, of which 1.7 [95% confidence intervals 1.5; 1.9] years and 2.7 [2.4; 2.9] years with cognitive impairment. Both black men and women live 3.7 [2.9; 4.5] years longer with cognitive impairment than whites, Hispanic men and women 3.2 [1.9; 4.6] and 5.8 [4.2; 7.5] years. BMI makes no difference. Smoking decreases the duration of cognitive impairment with 0.8 [0.4; 1.3] years by high mortality. Highly educated men and women live longer, but 1.6 years [1.1; 2.2] and 1.9 years [1.6; 2.6] shorter with cognitive impairment than lowly educated men and women. The effect of education is more pronounced among ethnic minorities. Higher life expectancy goes together with a longer period of cognitive impairment, but not for higher levels of education: that extends life in good cognitive health but shortens the period of cognitive impairment. The increased duration of cognitive impairment in minority ethnic groups needs further study, also in Europe

Disability weights for comorbidity and their influence on Health-adjusted Life Expectancy

BACKGROUND: Comorbidity complicates estimations of health-adjusted life expectancy (HALE) using disease prevalences and disability weights from Burden of Disease studies. Usually, the exact amount of comorbidity is unknown and no disability weights are defined for comorbidity. METHODS: Using data of the Dutch national burden of disease study, the effects of different methods to adjust for comorbidity on HALE calculations are estimated. The default multiplicative adjustment method to define disability weights for comorbidity is compared to HALE estimates without adjustment for comorbidity and to HALE estimates in which the amount of disability in patients with multiple diseases is solely determined by the disease that leads to most disability (the maximum adjustment method). To estimate the amount of comorbidity, independence between diseases is assumed. RESULTS: Compared to the multiplicative adjustment method, the maximum adjustment method lowers HALE estimates by 1.2 years for males and 1.9 years for females. Compared to no adjustment, a multiplicative adjustment lowers HALE estimates by 1.0 years for males and 1.4 years for females. CONCLUSION: The differences in HALE caused by the different adjustment methods demonstrate that adjusting for comorbidity in HALE calculations is an important topic that needs more attention. More empirical research is needed to develop a more general theory as to how comorbidity influences disability