Projecting the burden of dental caries and periodontal diseases among the adult population in the United Kingdom using a multi-state population model.

Objectives: With the aging United Kingdom population, oral diseases are expected to increase. Exploring credible projections is fundamental to understanding the likely impact of emerging population-level interventions on oral disease burden. This study aims at providing a credible, evidence-based projection of the adult population in the United Kingdom with dental caries and periodontal diseases. Methods: We developed a multi-state population model using system dynamics that disaggregates the adult population in the United Kingdom into different oral health states. The caries population was divided into three states: no caries, treated caries, and untreated caries. The periodontal disease population was disaggregated into no periodontal disease, pocketing between 4 and < 6 mm, 6 and < 9 mm, and 9 mm or more. Data from the 2009 dental health survey in the United Kingdom was used to estimate age and gender-specific prevalence rates as input to the multi-state population model. Results: Of the population 16 years and older, the number with carious teeth is projected to decrease from 15.742 million in the year 2020 to 15.504 million by the year 2050, representing a decrease of 1.5%. For individuals with carious teeth, the older adult population is estimated to constitute 62.06% by 2050 and is projected to increase 89.4% from 5.079 million in 2020 to 9.623 million by 2050. The adult population with periodontal pocketing is estimated to increase from 25.751 million in 2020 to 27.980 million by 2050, while those with periodontal loss of attachment are projected to increase from 18.667 million in 2020 to 20.898 million by 2050. The burden of carious teeth and periodontal diseases is anticipated to shift from the adult population (16-59 years) to the older adult population. The older adult population with carious teeth is estimated to rise from 32.26% in 2020 to 62.06% by 2050, while that for periodontal disease is expected to increase from 42.44% in 2020 to 54.57% by 2050. Conclusion: This model provides evidence-based plausible future demand for oral health conditions, allowing policymakers to plan for oral health capacity to address growing needs. Because of the significant delay involved in educating and training oral health personnel, such projections offer policymakers the opportunity to be proactive in planning for future capacity needs instead of being reactive.

Projection of Eye Disease Burden in Singapore.

INTRODUCTION: Singapore's ageing population is likely to see an increase in chronic eye conditions in the future. This study aimed to estimate the burden of eye diseases among resident Singaporeans stratified for age and ethnicity by 2040. MATERIALS AND METHODS: Prevalence data on myopia, epiretinal membrane (ERM), retinal vein occlusion (RVO), age macular degeneration (AMD), diabetic retinopathy (DR), cataract, glaucoma and refractive error (RE) by age cohorts and educational attainment from the Singapore Epidemiology of Eye Diseases (SEED) study were applied to population estimates from the Singapore population model. RESULTS: All eye conditions are projected to increase by 2040. Myopia and RE will remain the most prevalent condition, at 2.393 million (2.32 to 2.41 million) cases, representing a 58% increase from 2015. It is followed by cataract and ERM, with 1.33 million (1.31 to 1.35 million), representing an 81% increase, and 0.54 million (0.53 to 0.549 million) cases representing a 97% increase, respectively. Eye conditions that will see the greatest increase from 2015 to 2040 in the Chinese are: DR (112%), glaucoma (100%) and ERM (91.4%). For Malays, DR (154%), ERM (136%), and cataract (122%) cases are expected to increase the most while for Indians, ERM (112%), AMD (101%), and cataract (87%) are estimated to increase the most in the same period. CONCLUSION: Results indicate that the burden for all eye diseases is expected to increase significantly into the future, but at different rates. These projections can facilitate the planning efforts of both policymakers and healthcare providers in the development and provision of infrastructure and resources to adequately meet the eye care needs of the population. By stratifying for age and ethnicity, high risk groups may be identified and targeted interventions may be implemented.

Future requirements for and supply of ophthalmologists for an aging population in Singapore.

BACKGROUND: Singapore's population, as that of many other countries, is aging; this is likely to lead to an increase in eye diseases and the demand for eye care. Since ophthalmologist training is long and expensive, early planning is essential. This paper forecasts workforce and training requirements for Singapore up to the year 2040 under several plausible future scenarios. METHODS: The Singapore Eye Care Workforce Model was created as a continuous time compartment model with explicit workforce stocks using system dynamics. The model has three modules: prevalence of eye disease, demand, and workforce requirements. The model is used to simulate the prevalence of eye diseases, patient visits, and workforce requirements for the public sector under different scenarios in order to determine training requirements. RESULTS: Four scenarios were constructed. Under the baseline business-as-usual scenario, the required number of ophthalmologists is projected to increase by 117% from 2015 to 2040. Under the current policy scenario (assuming an increase of service uptake due to increased awareness, availability, and accessibility of eye care services), the increase will be 175%, while under the new model of care scenario (considering the additional effect of providing some services by non-ophthalmologists) the increase will only be 150%. The moderated workload scenario (assuming in addition a reduction of the clinical workload) projects an increase in the required number of ophthalmologists of 192% by 2040. Considering the uncertainties in the projected demand for eye care services, under the business-as-usual scenario, a residency intake of 8-22 residents per year is required, 17-21 under the current policy scenario, 14-18 under the new model of care scenario, and, under the moderated workload scenario, an intake of 18-23 residents per year is required. CONCLUSIONS: The results show that under all scenarios considered, Singapore's aging and growing population will result in an almost doubling of the number of Singaporeans with eye conditions, a significant increase in public sector eye care demand and, consequently, a greater requirement for ophthalmologists.

Projection of young-old and old-old with functional disability: does accounting for the changing educational composition of the elderly population make a difference?

This study compares projections, up to year 2040, of young-old (aged 60-79) and old-old (aged 80+) with functional disability in Singapore with and without accounting for the changing educational composition of the Singaporean elderly. Two multi-state population models, with and without accounting for educational composition respectively, were developed, parameterized with age-gender-(education)-specific transition probabilities (between active, functional disability and death states) estimated from two waves (2009 and 2011) of a nationally representative survey of community-dwelling Singaporeans aged ≥ 60 years (N=4,990). Probabilistic sensitivity analysis with the bootstrap method was used to obtain the 95% confidence interval of the transition probabilities. Not accounting for educational composition overestimated the young-old with functional disability by 65 percent and underestimated the old-old by 20 percent in 2040. Accounting for educational composition, the proportion of old-old with functional disability increased from 40.8 percent in 2000 to 64.4 percent by 2040; not accounting for educational composition, the proportion in 2040 was 49.4 percent. Since the health profiles, and hence care needs, of the old-old differ from those of the young-old, health care service utilization and expenditure and the demand for formal and informal caregiving will be affected, impacting health and long-term care policy.

Implications of long-term care capacity response policies for an aging population: a simulation analysis.

INTRODUCTION: The demand for long-term care (LTC) services is likely to increase as a population ages. Keeping pace with rising demand for LTC poses a key challenge for health systems and policymakers, who may be slow to scale up capacity. Given that Singapore is likely to face increasing demand for both acute and LTC services, this paper examines the dynamic impact of different LTC capacity response policies, which differ in the amount of time over which LTC capacity is increased, on acute care utilization and the demand for LTC and acute care professionals. METHODS: The modeling methodology of System Dynamics (SD) was applied to create a simplified, aggregate, computer simulation model for policy exploration. This model stimulates the interaction between persons with LTC needs (i.e., elderly individuals aged 65 years and older who have functional limitations that require human assistance) and the capacity of the healthcare system (i.e., acute and LTC services, including community-based and institutional care) to provide care. Because the model is intended for policy exploration, stylized numbers were used as model inputs. To discern policy effects, the model was initialized in a steady state. The steady state was disturbed by doubling the number of people needing LTC over the 30-year simulation time. Under this demand change scenario, the effects of various LTC capacity response policies were studied and sensitivity analyses were performed. RESULTS: Compared to proactive and quick adjustment LTC capacity response policies, slower adjustment LTC capacity response policies (i.e., those for which the time to change LTC capacity is longer) tend to shift care demands to the acute care sector and increase total care needs. CONCLUSIONS: Greater attention to demand in the acute care sector relative to demand for LTC may result in over-building acute care facilities and filling them with individuals whose needs are better suited for LTC. Policymakers must be equally proactive in expanding LTC capacity, lest unsustainable acute care utilization and significant deficits in the number of healthcare professionals arise. Delaying LTC expansion could, for example, lead to increased healthcare expenditure and longer wait lists for LTC and acute care patients.

Simulating the impact of long-term care policy on family eldercare hours.

OBJECTIVE: To understand the effect of current and future long-term care (LTC) policies on family eldercare hours for older adults (60 years of age and older) in Singapore. DATA SOURCES: The Social Isolation Health and Lifestyles Survey, the Survey on Informal Caregiving, and the Singapore Government's Ministry of Health and Department of Statistics. STUDY DESIGN: An LTC Model was created using system dynamics methodology and parameterized using available reports and data as well as informal consultation with LTC experts. PRINCIPAL FINDINGS: In the absence of policy change, among the elderly living at home with limitations in their activities of daily living (ADLs), the proportion of those with greater ADL limitations will increase. In addition, by 2030, average family eldercare hours per week are projected to increase by 41 percent from 29 to 41 hours. All policy levers considered would moderate or significantly reduce family eldercare hours. CONCLUSION: System dynamics modeling was useful in providing policy makers with an overview of the levers available to them and in demonstrating the interdependence of policies and system components.