Targeted versus universal prevention. a resource allocation model to prioritize cardiovascular prevention

Impact of a Parent Education Program Delivered by Nurses and Health Care Providers in Reducing Infant Physical Abuse Hospitalization Rates in British Columbia, Canada

BACKGROUND

The Period of PURPLE Crying Program® (PURPLE) is a universal parent education program that is delivered by nurses and health care providers to all parents/caregivers of newborns in British Columbia (B.C.). The aim of the program is to reduce the incidence of Traumatic Head Injury -Child Maltreatment (THI-CM), a form of child physical abuse.

OBJECTIVE

To determine if the PURPLE program had an impact on the rate of physical abuse hospitalizations for children less than or equal to 24 months of age in B.C. since implementation in 2009.

METHODS

The analysis measured physical abuse hospitalization rates for the period January 1, 1999 to December 31, 2019 and excluded any cases of confirmed Traumatic Head Injury-Child Maltreatment. Data were divided into pre-implementation period January, 1999 to December, 2008, and post-implementation period January, 2009 to December, 2019. Data were obtained from the Discharge Abstract Database and B.C. THI-CM Surveillance System to capture information on infant child abuse. Poisson regression and ANCOVA was applied to model the change in rates pre and post program implementation.

RESULTS

Physical abuse hospitalization rates decreased by 30% post-implementation period (95% CI: -14%, 57%, p  =  0.1561). The decreasing linear trend in the post-implementation period was significantly different than the increasing linear trend in the pre-implementation period (F1,17  =  4.832, p  =  0.042).

CONCLUSIONS

Nurses' role in engaging parents in conversations about PURPLE messages over multiple timepoints within a structured universal program model resulted in a decrease in physical abuse hospitalization rates since the implementation of PURPLE.

Health care input constraints and cost effectiveness analysis decision rules

Results of cost effectiveness analyses (CEA) studies are most useful for decision makers if they face only one constraint: the health care budget. However, in practice, decision makers wishing to use the results of CEA studies may face multiple resource constraints relating to, for instance, constraints in health care inputs such as a shortage of skilled labour. The presence of multiple resource constraints influences the decision rules of CEA and limits the usefulness of traditional CEA studies for decision makers. The goal of this paper is to illustrate how results of CEA can be interpreted and used in case a decision maker faces a health care input constraint. We set up a theoretical model describing the optimal allocation of the health care budget in the presence of a health care input constraint. Insights derived from that model were used to analyse a stylized example based on a decision about a surgical robot as well as a published cost effectiveness study on eye care services in Zambia. Our theoretical model shows that applying default decision rules in the presence of a health care input constraint leads to suboptimal decisions but that there are ways of preserving the traditional decision rules of CEA by reweighing different cost categories. The examples illustrate how such adjustments can be made, and makes clear that optimal decisions depend crucially on such adjustments. We conclude that it is possible to use the results of cost effectiveness studies in the presence of health care input constraints if results are properly adjusted.

Cost-effectiveness of population-based, community, workplace and individual policies for diabetes prevention in the UK

AIM

To analyse the cost-effectiveness of different interventions for Type 2 diabetes prevention within a common framework.

METHODS

A micro-simulation model was developed to evaluate the cost-effectiveness of a range of diabetes prevention interventions including: (1) soft drinks taxation; (2) retail policy in socially deprived areas; (3) workplace intervention; (4) community-based intervention; and (5) screening and intensive lifestyle intervention in individuals with high diabetes risk. Within the model, individuals follow metabolic trajectories (for BMI, cholesterol, systolic blood pressure and glycaemia); individuals may develop diabetes, and some may exhibit complications of diabetes and related disorders, including cardiovascular disease, and eventually die. Lifetime healthcare costs, employment costs and quality-adjusted life-years are collected for each person.

RESULTS

All interventions generate more life-years and lifetime quality-adjusted life-years and reduce healthcare spending compared with doing nothing. Screening and intensive lifestyle intervention generates greatest lifetime net benefit (£37) but is costly to implement. In comparison, soft drinks taxation or retail policy generate lower net benefit (£11 and £11) but are cost-saving in a shorter time period, preferentially benefit individuals from deprived backgrounds and reduce employer costs.

CONCLUSION

The model enables a wide range of diabetes prevention interventions to be evaluated according to cost-effectiveness, employment and equity impacts over the short and long term, allowing decision-makers to prioritize policies that maximize the expected benefits, as well as fulfilling other policy targets, such as addressing social inequalities.

Modelling the economics of type 2 diabetes mellitus prevention: a literature review of methods

Our objective was to review modelling methods for type 2 diabetes mellitus prevention cost-effectiveness studies. The review was conducted to inform the design of a policy analysis model capable of assisting resource allocation decisions across a spectrum of prevention strategies. We identified recent systematic reviews of economic evaluations in diabetes prevention and management of obesity. We extracted studies from two existing systematic reviews of economic evaluations for the prevention of diabetes. We extracted studies evaluating interventions in a non-diabetic population with type 2 diabetes as a modelled outcome, from two systematic reviews of obesity intervention economic evaluations. Databases were searched for studies published between 2008 and 2013. For each study, we reviewed details of the model type, structure, and methods for predicting diabetes and cardiovascular disease. Our review identified 46 articles and found variation in modelling approaches for cost-effectiveness evaluations for the prevention of type 2 diabetes. Investigation of the variables used to estimate the risk of type 2 diabetes suggested that impaired glucose regulation, and body mass index were used as the primary risk factors for type 2 diabetes. A minority of cost-effectiveness models for diabetes prevention accounted for the multivariate impacts of interventions on risk factors for type 2 diabetes. Twenty-eight cost-effectiveness models included cardiovascular events in addition to type 2 diabetes. Few cost-effectiveness models have flexibility to evaluate different intervention types. We conclude that to compare a range of prevention interventions it is necessary to incorporate multiple risk factors for diabetes, diabetes-related complications and obesity-related co-morbidity outcomes.

Modelling the cost-effectiveness of preventing major depression in general practice patients

BACKGROUND

The prevention of depression is a key public health policy priority. PredictD is the first risk algorithm for the prediction of the onset of major depression. Our aim in this study was to model the cost-effectiveness of PredictD in depression prevention in general practice (GP).

METHOD

A decision analytical model was developed to determine the cost-effectiveness of two approaches, each of which was compared to treatment as usual (TAU) over 12 months: (1) the PredictD risk algorithm plus a low-intensity depression prevention programme; and (2) a universal prevention programme in which there was no initial identification of those at risk. The model simulates the incidence of depression and disease progression over 12 months and calculates the net monetary benefit (NMB) from the National Health Service (NHS) perspective.

RESULTS

Providing patients with PredictD and a depression prevention programme prevented 15 (17%) cases of depression in a cohort of 1000 patients over 12 months and had the highest probability of being the optimal choice at a willingness to pay (WTP) of £20,000 for a quality-adjusted life year (QALY). Universal prevention was strongly dominated by PredictD plus a depression prevention programme in that universal prevention resulted in less QALYs than PredictD plus prevention for a greater cost.

CONCLUSIONS

Using PredictD to identify primary-care patients at high risk of depression and providing them with a low-intensity prevention programme is potentially cost-effective at a WTP of £20,000 per QALY.

Impact of behaviour and lifestyle on bladder health

Bladder conditions, including UTI, UI, and bladder cancer, are highly prevalent and affect a wide range of populations. There are a variety of modifiable behavioral and lifestyle factors that influence bladder health. Some factors, such as smoking and obesity, increase the risk or severity of bladder conditions, whereas other factors, such as pelvic floor muscle exercise, are protective. Although clinical practice may be assumed to be the most appropriate ground for education on behavioral and lifestyle factors that influence bladder health, it is also crucial to extend these messages into the general population through public health interventions to reach those who have not yet developed bladder conditions and to maximize the prevention impact of these behaviors. Appropriate changes in these factors have the potential for an enormous impact on bladder health if implemented on a population-based level.