The influence of distinct factors on the evolution of health spending and life expectancy is illustrated for 2015-2020. For example, an outbreak of a new disease can drive a decline in life expectancy and an increase in health-care costs, as shown by vector 1. Improvements in education, housing, nutrition, or other social determinants of health could drive increases in life expectancy without necessarily affecting health-care costs (vector 4). The adoption and coverage of new interventions will influence both life expectancy and costs. Health interventions affect both costs and benefits in the specific group of patients or subpopulation to which they are directed, affecting the evolution of health expenditures and life expectancy or HALE at the population level. A direct relationship exists between the ICER of the new interventions and the type of influence they will exert. For example, the adoption and coverage of a cost-saving intervention will produce health benefits and cost savings in the specific subgroup of patients to whom it is applied. These effects will also result in an increase in the life expectancy (or HALE) of the population and a reduction of health expenditure per capita at the country level or health-system level (vector 5), even though the effect of a single intervention at the population level will probably be minimal. On the other hand, an intervention that is more effective but more costly (ie, with an ICER in the upper-right quadrant of the cost-effectiveness plane) will improve life expectancy (or HALE) and increase health expenditures. An intervention with a more unfavourable ICER (vector 2) will be associated with a higher increase in health expenditures than a more cost-effective intervention (vector 3). This is reflected in the direction (slope) of the vectors (eg, vector 2 is steeper than vector 3). The ICER of the intervention determines the slope. Two interventions with the same ICER will be colinear vectors (ie, will have the same slope), although they could have different magnitude. An intervention aimed at a larger proportion of the population will be a vector of greater magnitude because it will be more influential. The slope (direction) of a vector (m) is calculated by finding the ratio of the vertical change to the horizontal change between two distinct points on the line. In this case, the vertical change will be the percentage increase in health expenditures per capita at the population level (%Δh) in a given period, and the horizontal change will be the corresponding change in life expectancy (ΔLE). For example, between 2015 and 2020, the resultant vector of all factors affecting health expenditures and life expectancy has a slope of m=0.16, as during this period all factors affecting health expenditures and life expectancy resulted in a change in life expectancy from 75 to 76 years (ΔLE=1 year), and a 16% change in health expenditure per capita, from US$549 to $639 (%Δh=0.16). The white and grey arrows show projections for the future. The white arrow is the expected resultant vector of all the known and unknown factors affecting health expenditure per capita and life expectancy during the time period. The grey arrow shows that the vector of influence of the new interventions will be colinear with the expected resultant vector if the ICER of new interventions is equal to m HEpc LE (equation 2). HALE=health-adjusted life expectancy. HEpc=annual total health expenditure per capita. ICER=incremental cost-effectiveness ratio.
00162-6%2Ffig1_a.webp&w=1920&q=75 "Learn How to Make Line Plot for Your Research Paper")