Disaggregating proportional multistate lifetables by population heterogeneity to estimate intervention impacts on inequalities

Abstract

Background Simulation models can be used to quantify the projected health impact of interventions. Quantifying heterogeneity in these impacts, for example by socioeconomic status, is important to understand impacts on health inequalities. We aim to disaggregate one type of Markov macro-simulation model, the proportional multistate lifetable, ensuring that under business-as-usual (BAU) the sum of deaths across disaggregated strata in each time step returns the same as the initial non-disaggregated model. We then demonstrate the application by deprivation quintiles for New Zealand (NZ), for: hypothetical interventions (50% lower all-cause mortality, 50% lower coronary heart disease mortality) and a dietary intervention to substitute 59% of sodium with potassium chloride in the food supply. Methods We developed a disaggregation algorithm that iteratively rescales mortality, incidence and case-fatality rates by time-step of the model to ensure correct total population counts were retained at each step. To demonstrate the algorithm on deprivation quintiles in NZ, we used the following inputs: overall (non-disaggregated) all-cause mortality & morbidity rates, coronary heart disease incidence & case fatality rates; stroke incidence & case fatality rates. We also obtained rate ratios by deprivation for these same measures. Given all-cause and cause-specific mortality rates by deprivation quintile, we derived values for the incidence, case fatality and mortality rates for each quintile, ensuring rate ratios across quintiles and the total population mortality and morbidity rates were returned when averaged across groups. The three interventions were then run on top of these scaled BAU scenarios. Results The algorithm exactly disaggregated populations by strata in BAU. The intervention scenario life years and health adjusted life years (HALYs) gained differed slightly when summed over the deprivation quintile compared to the aggregated model, due to the stratified model (appropriately) allowing for differential background mortality rates by strata. Modest differences in health gains (HALYs) resulted from rescaling of sub-population mortality and incidence rates to ensure consistency with the aggregate population. Conclusion Policy makers ideally need to know the effect of population interventions estimated both overall, and by socioeconomic and other strata. We demonstrate a method and provide code to do this routinely within proportional multistate lifetable simulation models and similar Markov models.

Health gains from achieving optimal body mass index in Australia: a simulation study

“…These sex by age rates were then disaggregated by the socio-economic index for areas (SEIFA), using rate ratios (RRs) derived from the AIHW burden of disease study 18 and a previously published heterogeneity module. 10 , 19 , 20 The SEIFA RRs were estimated with log-link regression models on AIHW data, by sex, separately for mortality (Poisson error) and prevalence of disease (binomial error), with main effects for age (five-year age categories) and SEIFA (quintiles coded as integer continuous) and an interaction term of age (aggregated categories 20–24, 25–29, … 80–84 and 85 + ) with integer SEIFA. These SEIFA differences in disease mortality and prevalence were then used to secondarily estimate SEIFA RRs for incidence and case fatality rates.…”