System and method for evaluating intervention options for falls and / or fractures

Abstract

The present disclosure provides systems and methods for evaluating a wide range of intervention options for falls and / or fractures in individuals. The disclosed method comprises the steps of receiving a first data set comprising at least one fall probability; receiving a second data set comprising at least one fracture probability; receiving a third data set comprising the plurality of intervention options for falls and / or fractures in individuals; computing a comparison value V for each intervention option l of the plurality of intervention options of the third data set; and determining an optimal intervention option based on the comparison value V of each intervention option l at a given fall probability and fracture probability.

Description

DESCRIPTIONTITLE OF INVENTION: SYSTEM AND METHOD FOR EVALUATING INTERVENTION OPTIONS FOR FALLS AND / OR FRACTURESTECHNICAL FIELD

[0001] The present disclosure relates generally to systems and methods for evaluating intervention options for falls and / or fractures in individuals.BACKGROUND

[0002] The following discussion of the background to the invention is intended to facilitate an understanding of one or more inventions disclosed by the present specification only. It should be appreciated that the discussion is not an acknowledgement or admission that any of the material referred to was published, known or part of the common general knowledge of the person skilled in the art in any jurisdiction as at the priority date of one or more inventions disclosed by the present specification.

[0003] Falls in older adults are a significant health problem. Falls result in major adverse physical and psychological consequences for older adults (Tinetti et aL, 1994; T. H. Wong et aL, 2019). A common, and severe physical consequence of a fall is a major osteoporotic fracture (Cummings et al., 1995). Hip fractures, out of all the fractures in older adults, result in the highest mortality and morbidity, with prolonged periods of disability (Haleem et aL, 2008; Kanis et aL, 2003). Fractures also pose a large burden to the health system, caregivers, and family members, in addition to the older persons themselves. Thus, there is an impetus to prevent or reduce fractures in older adults through falls prevention programs. However, while an estimated 95% of fractures are the direct consequence of a fall, only 1-5% of falls result in a fracture (Cummings et aL, 1995; Parkkari et aL, 1999). In fact, many fallers do not even seek medical attention from a healthcare provider after a recent fall (Stevens et aL, 2012). Thus, there is still uncertainty as to whom to target for which of one or more intervention can reduce fracture burden.

[0004] Current clinically recommended approaches to fracture risk reduction involve the use of fracture risk prediction algorithms followed by treatment with pharmacological therapies that increase bone mineral density if indicated by a fracture risk algorithm. These therapies include anti-resorptive drugs, vitamin D and calcium supplements, and other biologies that promote bone formation. The value of using of these therapies, depends on the predictive performance of the risk prediction algorithm to identify which individuals will benefit. While there have been clinical tools that are recommended for screening like the Osteoporosis Self-Assessment Tool for Asians(OSTA) and the Fracture Risk Assessment Tool (FRAX), these tools have poor sensitivity and specificity in predicting fractures in older adults. Furthermore, these tools do not consider or adequately consider the risk of falls. For example, FRAX considers falls risk in its fracture risk algorithm only by including falls history as a fracture risk factor (Kanis et al., 2001). However, research has shown that falls history is not sensitive to predicting falls (Burns et al., 2022; Rodrfguez-Molinero et aL, 2017). As a consequence of the FRAX use of history of past falls to judge future falls risk, most older people put on bone strengthening therapies receive treatment only after they are presented to the health system with a fracture (Edwards et aL, 2007; Klotzbuecher et aL, 2000). Predicting falls in older adults who have not fallen or have had a fall without fracture is complicated by the variety of factors that can lead or interact to lead to a fall. In addition to not reliably accounting for these factors, current risk algorithms do not account for other factors such as cost, preferences for avoiding adverse health outcomes, and local policy context such as societal preferences for avoiding adverse outcomes and available resources, such as a local threshold value for an acceptable trade-off of cost to health outcomes (e.g. costeffectiveness).

[0005] Therefore, there exists a need to provide systems and methods to evaluate intervention options for falls and / or fractures in individuals and alleviate at least one of the aforementioned problems.SUMMARY

[0006] According to an aspect of the present disclosure, there is a method of evaluating a plurality of intervention options for falls and / or fractures in individuals, wherein the method comprises the steps of: receiving a first data set comprising at least one fall probability; receiving a second data set comprising at least one fracture probability; receiving a third data set comprising the plurality of intervention options for falls and / or fractures in individuals; computing a comparison value V for each intervention option / of the plurality of intervention options of the third data set, wherein the comparison value V is determined by the following expression:wherePfeiij comprises a given fall probability of the first data set; Pfracture_i coimprises a given fracture probability of the second data set;Ck comprises a plurality of characteristics relating to falls and fractures in a region £Ti comprises a plurality of characteristics of each intervention option / of the third data set; and determining an optimal intervention option based on the comparison value V of each intervention option I at Pfall_i and Pfracture_i

[0007] In an example, the step of determining an optimal intervention option comprises comparing the comparison values V of the plurality of intervention options and determining the optimal intervention option based on a maximum comparison value Vmax.

[0008] In an example, the second data set comprises at least one hip fracture probability and Pfracture_i comprises a given hip fracture probability.

[0009] In an example, the plurality of characteristics relating to falls and fractures Ck comprises two or more of: an incidence of falls and an incidence of fractures of individuals in region k, wherein optionally the incidence of falls and the incidence of fractures are based on data from epidemiological studies, and optionally the data from epidemiological studies is based on age and / or gender; a healthcare cost associated with treating an individual in region kfor a fall and / or a fracture; and a mortality rate associated with a fall and / or fracture of individuals in region k, wherein optionally, the mortality rate is based on a life table and an excess mortality associated with a fall and / or fracture in region k.

[0010] In an example, the plurality of characteristics relating to falls and fractures Ck further comprises one or more of: a health benefit measure of individuals in region k, wherein optionally the health benefit measure is a non-monetary measure; anda willingness to pay (WTP) measure for health benefits in region k.

[0011] In an example, the plurality of characteristics Ti of each intervention option I comprises: an intervention cost; and an intervention effectiveness measure based on the effectiveness of each intervention option / to reduce an or the incidence of falls and / or an or the incidence of fractures of individuals in region k, wherein optionally the intervention effectiveness measure is based on data from one or more clinical trials.

[0012] In an example, the comparison value V is based on: a function of a health benefit associated with each intervention option / , and a function of a cost associated with each intervention option / , wherein the function of a health benefit and the function of a cost associated with each intervention option I is based on Ck and Ti.

[0013] In an example, the comparison value V is determined by equation (1):V = X - Y - Z (1) whereY provides the function of a health benefit associated with each intervention option I and comprises one or more measurable health outcomes associated with each intervention option I in region k, the one or more measurable health outcomes accounting for health state desirability and survival;A comprises a or the willingness to pay (WTP) measure per unit of health benefit in region k\ andZ provides the function of a cost associated with each intervention option I and comprises at least a or the healthcare cost and a or the intervention cost associated with each intervention option / .

[0014] In an example, the plurality of intervention options of the third data set comprises two or more of: (i) no treatment option, (ii) a bisphosphonate only option, (iii) a falls prevention program option, and (iv) a both bisphosphonate and falls prevention program option.

[0015] In an example, the comparison value V is determined based on one or more of a Markov cohort model, a simple decision tree, a discrete event simulation, a system dynamics simulation, an agent based model, a statistical fitting and an expert panel-based method, wherein optionally, the comparison value V is determined based on a Markov cohort model.

[0016] In an example, the method comprises evaluating a plurality of intervention options for falls and / or fractures in an individual, and wherein the first data set comprises a fall probability of the individual andcomprises the fall probability of the individual; and the second data set comprises a fracture probability of the individual andcomprises the fracture probability of the individual.

[0017] In an example, the method comprises evaluating a plurality of intervention options for falls and / or fractures in a population, and wherein the first data set comprises a plurality of fall probabilities; and the second data set comprises a plurality of fracture probabilities, and wherein the method further comprises generating a results data set comprising a plurality of the optimal intervention option determined at each

[0018] In an example, the method further comprises plottingand Vmax as a three-dimensional plot.

[0019] In an example, the method further comprises plottingand the optimal intervention option atas a two-dimensional plot based on Vmax.

[0020] In an example, same optimal intervention options at differentare represented with a common identifier, wherein optionally, the common identifier comprises a colour or tone.

[0021] In an example, same optimal intervention options at differentare provided as a zone on the two-dimensional plot, the two-dimensional plot comprising one or more zones, each representing an optimal intervention option.

[0022] In an example, the two-dimensional plot comprises a plurality of zones and adjacent zones are separated by a line of equivalence, wherein the line of equivalence provides that the optimal intervention options in the adjacent zones have substantially equivalent effect at aparticular Pfall_i and Pfracture_i

[0023] In an example, the method further comprises receiving an individual's fall and fracture probabilities based on a fall risk assessment and / or a fracture risk assessment, wherein the fall risk assessment comprises one or more of a questionnaire-based falls risk screener, a functional assessment and a gait assessment, and the fracture risk assessment comprises one or more of clinical risk factors assessment and fracture risk imaging.

[0024] In an example, the method further comprises referencing the results data set with the individual’s fall and fracture probabilities to determine the optimal intervention option for the individual.

[0025] In an example, the method further comprises computing an expected value E(V) based on two or more comparison values Vof the plurality of intervention options of the third data set, wherein the method comprises using the expected value E(V) to evaluate one or more falls and fractures risk screening tools and / or evaluating one or more new intervention options.

[0026] According to another aspect of the present disclosure, there is a system for evaluating a plurality of intervention options for falls and / or fractures in individuals, wherein the system comprises a processor configured to receive: a first data set comprising at least one fall probability; a second data set comprising at least one fracture probability; a third data set comprising the plurality of intervention options for falls and / or fractures in individuals, the processor configured to compute a comparison value V for each intervention option I of the plurality of intervention options of the third data set, wherein the comparison value V is determined by the following expression:where Pfall_i comprises a given fall probability of the first data set;Pfrecture comprises a given fracture probability of the second data set;CR comprises a plurality of characteristics relating to falls and fractures in a regionk;Ti comprises a plurality of characteristics of each intervention option / of the third data set, wherein the processor is configured to determine an optimal intervention option based on the comparison value V of each intervention option I at Pfall_i and Pfracture_i

[0027] In an example, the processor is configured to determine the optimal intervention option based on a maximum comparison value Vmax.

[0028] In an example, the plurality of characteristics relating to falls and fractures Ck comprises two or more of: an incidence of falls and an incidence of fractures of individuals in region k, wherein optionally the incidence of falls and the incidence of fractures are based on data from epidemiological studies, and optionally the data from epidemiological studies is based on age and / or gender; a healthcare cost associated with treating an individual in region k for a fall and / or a fracture; and a mortality rate associated with a fall and / or fracture of individuals in region k, wherein optionally, the mortality rate is based on a life table and an excess mortality associated with a fall and / or fracture in region k.

[0029] In an example, the plurality of characteristics relating to falls and fractures Ck further comprises one or more of: a health benefit measure of individuals in region k, wherein optionally the health benefit measure is a non-monetary measure; and a willingness to pay (WTP) measure for health benefits in region k.

[0030] In an example, the plurality of characteristics 7} of each intervention option / comprises an intervention cost; and an intervention effectiveness measure based on the effectiveness of each intervention option I to reduce an or the incidence of falls and / or an or the incidence of fractures of individuals in region k, wherein optionally the intervention effectiveness measure isbased on data from one or more clinical trials.

[0031] In an example, the comparison value V is based on: a function of a health benefit associated with each intervention option I, and a function of a cost associated with each intervention option / , wherein the function of a health benefit and the function of a cost associated with each intervention option I is based on Ck and 7}.

[0032] In an example, the processor is configured to determine the comparison value V based on equation (1):V = - Y - Z (1) whereY provides the function of a health benefit associated with each intervention option I and comprises one or more measurable health outcomes associated with each intervention option I in region k, the one or more measurable health outcomes accounting for health state desirability and survival;A comprises a or the willingness to pay (WTP) measure per unit of health benefit in region k; andZ provides the function of a cost associated with each intervention option I and comprises at least a or the healthcare cost and a or the intervention cost associated with each intervention option / .

[0033] In an example, the plurality of intervention options of the third data set comprises two or more of: (i) no treatment option, (ii) a bisphosphonate only option, (ill) a falls prevention program option, and (iv) a both bisphosphonate and falls prevention program option.

[0034] In an example, the system comprises an input device, the processor configured to receive the first and / or second data set from the input device, wherein the input device comprises one or more of a user interface, storage medium, a sensor and an imaging device.

[0035] In an example, the system comprises an output device, the processor configured to output the optimal intervention option to the output device, wherein optionally, the output device comprises a or the user interface.

[0036] According to another aspect of the present disclosure, there is a non-transitory computer-readable medium having computer executable instructions stored thereon, that when executed by a processor oron a device, cause the processor or the device to perform any method of the present disclosure for evaluating a plurality of intervention options for falls and / or fractures in individuals, wherein the method comprises: receiving a first data set comprising at least one fall probability; receiving a second data set comprising at least one fracture probability; receiving a third data set comprising the plurality of intervention options for falls and / or fractures in individuals; computing a comparison value V for each intervention option / of the plurality of intervention options of the third data set, wherein the comparison value V is determined by the following expression:where Pfall_i comprises a given fall probability of the first data set; Pfracture_i comprises a given fracture probability of the second data set;CR comprises a plurality of characteristics relating to falls and fractures in a region k;Ti comprises a plurality of characteristics of each intervention option / of the third data set; and determining an optimal intervention option based on the comparison value V of each intervention option I at Pfall_i and Pfractujre-_i

[0037] Other aspects and features of the present invention will become apparent to those of ordinary skill in the art upon review of the following description of specific embodiments of the disclosure in conjunction with the accompanying figures.BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In the figures, which illustrate, by way of non-limiting examples only, embodiments of the present disclosure,

[0039] [Fig. 1]: A process flow diagram of using an embodiment of the present disclosure.

[0040] [Fig. 2]: A process flow diagram of using an embodiment of the present disclosure based on Example 1 .

[0041] [Fig. 3]: Decision Tree structure for four Markov models that evaluate the NMB over five years at different probabilities of falls and fractures for Singaporean older adults. The Markov models track the costs and disutilities for each fall and fracture event.

[0042] [Fig. 4]: State transition diagram summarizing the potential transitions between the five states in the Markov model. Post falls / fractures states account for long-term disutilities and costs associated with a fall and fracture, and the increased probability of subsequent falls and fractures.

[0043] [Fig. 5]: Three-dimensional plots of NMB across the full range of falls and fractures probabilities for (a) doing nothing, (b) bisphosphonates only, (c) falls prevention program only, and (d) both bisphosphonates and falls prevention programs.

[0044] [Fig. 6]: Contour two-dimensional plots of NMB across the full range of falls and fractures probabilities for (a) doing nothing, (b) bisphosphonates only, (c) falls prevention program only, and (d) both bisphosphonates and falls prevention programs. Lighter tones denote a lower NMB, while darker tones denote a higher NMB.

[0045] [Fig. 7]: Plots of optimal treatment decisions for each probability of falls and fractures at willingness-to-pay (WTP) threshold of (a) $0, (b) $15 000, (c) $45 000, and (d) $75 000 respectively. The different tones represent the treatment decision at that point with the highest incremental net monetary benefit.

[0046] [Fig. 8]: (a) A pre-test fall and fracture probability of “A”, and a post-test fall and fracture probability of “B” (positive test) and “C” (negative test) of an individual based on an exemplary screening tool is shown against a plot of optimal treatment decisions for each probability of falls and fractures in a region k at a willingness-to-pay (WTP) threshold of $75 000. (b) A pre-test fall and fracture probability of “D", and a post-test fall and fracture probability of “E” (positive test) and “F” (negative test) of an individual based on another exemplary screening tool is shown against a plot of optimal treatment decisions for each probability of falls and fractures in a region k at a willingness-to-pay (WTP) threshold of $75 000.

[0047] [Fig. 9]: Plots of optimal Intervention options at each fall probability and fracture probability with a falls prevention program that reduces the rate of falls by (a) 36% and (b) 60%.

[0048] [Fig. 10]: A plot of optimal treatment decisions for each probability of falls and fractures at a willingness-to-pay (WTP) threshold of $75 000 with indications of the fall and fracture probabilities of two individuals G and H.DETAILED DESCRIPTION

[0049] As used herein, the term “(s)” following a noun means the plural and / or singular form of that noun.

[0050] As used herein, the term “and / or” means “and” or “or”, or where the context allows both.

[0051] Throughout this document, unless otherwise indicated to the contrary, the terms “comprising”, “consisting of’, “having” and the like, are to be construed as non-exhaustive, or in other words, as meaning “including, but not limited to”.

[0052] Furthermore, throughout the document, unless the context requires otherwise, the word “include” or variations such as “includes” or “including" will be understood to imply the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers.

[0053] As used herein, the term “individual” includes a human individual.

[0054] The phrase ‘computer-readable medium’ should be taken to include a single medium or multiple media. Examples of multiple media include a centralised or distributed database and / or associated caches. These multiple media store one or more sets of computer executable instructions. The phrase ‘computer readable medium’ should also be taken to include any medium that is capable of storing, encoding and / or carrying a set of instructions for execution by a processor of a computing device and that cause the processor or computing device to perform any one or more of the methods described herein. The computer-readable medium is also capable of storing, encoding and / or carrying data structures used by or associated with these sets of instructions. The phrase 'computer-readable medium’ includes but is not limited to solid-state memories, optical media and magnetic media.

[0055] It is intended that reference to any range of numbers disclosed herein (for example, 1 to 10) also incorporates reference to all rational numbers within that range (for example, 1 , 1.1 , 2, 3, 3.9, 4, 5, 6, 6.5, 7, 8, 9 and 10) and also any range of rational numbers within that range (for example, 2 to 8, 1.5 to 5.5 and 3.1 to 4.7) and, therefore, all sub-ranges of all ranges expressly disclosed herein are hereby expressly disclosed. These are only examples of what is specificallyintended and all possible combinations of numerical values between the lowest value and the highest value enumerated are to be considered to be expressly stated in this application in a similar manner.

[0056] This disclosure may also be said broadly to consist in the parts, elements and features referred to or indicated in the specification of the application, individually or collectively, and any or all combinations of any two or more said parts, elements or features, and where specific integers are mentioned herein which have known equivalents in the art to which this disclosure relates, such known equivalents are deemed to be incorporated herein as if individually set forth.

[0057] The embodiments may be herein described as a process that is depicted as a flowchart, a flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, some of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be rearranged. A process is terminated when its operations are completed. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc., in a computer program. When a process corresponds to a function, its termination corresponds to a return of the function to the calling function or a main function.

[0058] Furthermore, embodiments described herein may be implemented by hardware, software, firmware, middleware, microcode, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine-readable medium such as a storage medium or other storage(s). A processor may perform the necessary tasks. A code segment may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and / or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.

[0059] A storage medium may represent one or more devices for storing data, including readonly memory (ROM), random access memory (RAM), magnetic disk storage mediums, optical storage mediums, flash memory devices and / or other machine readable mediums for storing information. The terms “machine readable medium” and “computer readable medium” include, but are not limited to portable or fixed storage devices, optical storage devices, and / or various other mediums capable of storing, containing or carrying instruction(s) and / or data, including non- transitory mediums.

[0060] A processor may include one or more processors. A processor may be one or more of a general purpose processor and an application specific processor. A general purpose processor may be a microprocessor, a microcontroller, a circuit, and / or a digital signal processor (DSP). A processor may also be implemented as a combination of computing components, e.g., a combination of a DSP and a microprocessor, a number of microprocessors, or any other such configuration. A storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. The storage medium may be integral to the processor.

[0061] The embodiments described herein can be embodied in a computer-implemented process, a machine (such as an electronic device, or a general purpose computer or other device that provides a platform on which computer programs can be executed), processes performed by these machines, or an article of manufacture. Such articles can include a computer program product or digital information product in which a computer readable storage medium containing computer program instructions or computer readable data stored thereon, and processes and machines that create and use these articles of manufacture.

[0062] Unless defined otherwise, all other technical and scientific terms used herein have the same meaning as is commonly understood by a skilled person to which the subject matter herein belongs.

[0063] It should be further appreciated by the person skilled in the art that variations and combinations of features described above, not being alternatives or substitutes, may be combined to form yet further embodiments falling within the intended scope of the disclosure. As would be understood by a person skilled in the art, each embodiment, may be used in combination with other embodiment or several embodiments.

[0064] The foregoing description of example embodiments includes example forms and configurations. Modifications may be made without departing from the scope of the disclosure and / or accompanying claims.

[0065] The present disclosure relates to the evaluation of a plurality of intervention options for falls and / or fractures in individuals. The method, system and a computer-readable medium of the present disclosure may provide for evaluation of the usefulness of falls and factures screening tools in a program for screening falls and fractures in older individuals, decision on price thresholds to support falls and fractures screening, identification of gaps in health services with potential for improvement to maximise monetary benefit to a health system and / or any new intervention option for fall and / or fractures in individuals.

[0066] Additionally or alternatively, the present disclosure provides a method, a system and a computer-readable medium for evaluating a plurality of intervention options for falls and / or fractures in individuals and determining an optimal intervention option to reduce the risk of poor health outcomes associated with a fall and / or fracture based on such evaluation. The method, system and a computer-readable medium of the present disclosure may utilise decision modelling to investigate the cost-effectiveness of an intervention option that take into consideration a fall and / or fracture probability of an individual to determine the highest value therapeutic decision to reduce the risk of poor health outcomes associated with a fall and / or fracture. Embodiments of the present disclosure may be provided as a clinical decision tool to guide therapy for individual falls and fracture risk profiles.

[0067] [Fig. 1] provides a process flow diagram of using an exemplary clinical decision tool based on an embodiment of the present disclosure. At process step 101 , falls and / or fracture screening tools are evaluated and selected using the clinical decision tool based on data, for example characteristics (e.g. age) of individuals and epidemiological data of a region. The best falls and / or fracture screening tools may be selected. At process step 102, a falls risk assessment is conducted on an individual during for example a routine health check, to determine or estimate the individual’s fall probability. The falls risk assessment may be done by one or more of a questionnaire-based falls risk screener, functional assessments (for example, short physical performance battery, timed-up-and-go, 5 x sit to stand and 2 to 5 mins walk) and gait assessment using wearable sensors. At process step 103, a fracture risk assessment is conducted on the individual, e.g. during the same routine health check, to determine or estimate the individual’s fracture probability. The fracture risk assessment may be done by one or more of an assessment of clinical risk factors and imaging. While step 102 is shown to be performed before step 103, it will be appreciated that steps 102 and 103 may be conducted concurrently or step 103 may be performed before step 102. The individual's determined or estimated falls and fracture probabilities are thereafter provided as inputs into the clinical decision tool at process step 104. At process step 105, the clinical decision tool processes the inputs and provides an optimal intervention option as a recommendation to a user. The optimal intervention option may be provided as an output on a display to the user. In some examples, step 101 is optional and may be omitted.

[0068] Another process flow diagram of using a clinical decision tool based on Example 1 is provided in [Fig. 2], One or more falls risk assessment methods 201a, 202b are used to determine or estimate an individual’s fall probability 211. Method 201a may include use of sensors and method 201b may include use of questionnaires. One or more fracture risk assessment methods 202a, 202b are used to determine or estimate an individual's fracture probability 212. Method202a may include use of an imaging device and method 202b may include analysis of clinical risk factors. The individual’s determined or estimated falls and fracture probabilities are thereafter provided as inputs into a clinical decision tool 220 which is based on a Markov cohort model. The clinical decision 220 processes these inputs against a plurality of characteristics 221 relating to falls and fractures in a region and a plurality of characteristics 222 of each intervention option of a plurality of intervention options 231a-d that are available in the region. Intervention option 231a may be no treatment option; intervention option 231b may be a bisphosphonate only option; intervention option 231c may be a falls prevention program option; and intervention option 231 d may be a both bisphosphonate and falls prevention program option. The clinical decision tool 220 thereafter provides a recommendation of an optimal intervention option 231 a-d to a user.

[0069] An aspect of the present disclosure relates to a method of evaluating a plurality of intervention options for falls and / or fractures in individuals. The method comprises the steps of: receiving a first data set comprising at least one fall probability; receiving a second data set comprising at least one fracture probability; receiving a third data set comprising the plurality of intervention options for falls and / or fractures in individuals; computing a comparison value V for each intervention option I of the plurality of intervention options of the third data set, wherein the comparison value V is determined by the following expression:wherecomprises a given fall probability of the first data set;comprises a given fracture probability of the second data set;Ck comprises a plurality of characteristics relating to falls and fractures in a region ;Ti comprises a plurality of characteristics of each intervention option / of the third data set; and determining an optimal intervention option based on the comparison value V of each intervention option

[0070] As described in the present disclosure, a ‘region’ may be a population of people, a policy relevant area or a geographical area such as a country, a city and a town. A region may not have fixed boundaries. A policy relevant area may be one where there is a form of governance or policymaking to influence provision of health services for a population in that area. In an example, a region comprises a population of individuals where epidemiological information may be obtained.

[0071] One or more of the plurality of intervention options may be an intervention option available in region k. One or more of the plurality of intervention options may be an intervention option that is unavailable in region k but may be considered for region k. The plurality of intervention options may comprise two or more intervention options, three or more intervention options or four or more intervention options.

[0072] The step of determining an optimal intervention option may comprise comparing the comparison values V of the plurality of intervention options and determining the optimal intervention option based on a maximum comparison value Vmax. The maximum comparison value Vmax is taken to be the greatest (or highest) value among the plurality of intervention options at a Pfain and Pfmcturej. Vmax may be a positive or negative value.

[0073] The fracture probability in the second data set may be one or more of a hip fracture probability, vertebrae fracture probability, wrist fracture probability or other fracture probability. In an example, the second data set comprises at least one hip fracture probability and Pfracture_i comprises a given hip fracture probability.

[0074] The plurality of characteristics relating to falls and fractures C* may comprise two or more of: an incidence of falls and an incidence of fractures of individuals in region k, a healthcare cost associated with treating an individual in region k for a fall and / or a fracture and a mortality rate associated with a fall and / or fracture of individuals in region k. The incidence of falls and the incidence of fractures may be based on data from epidemiological studies. The data from epidemiological studies may be based on age and / or gender. The healthcare cost may comprise one or more of a direct medical cost, a direct non-medical cost and an indirect cost. A direct medical cost comprises one or more costs for providing testing and management of the results of the testing and subsequent health outcomes in a healthcare system, associated with a disease or health condition. Examples of a direct medical cost are diagnostic tests and physician visits. A direct non-medical cost comprises one or more costs outside of the healthcare system related to testing and management of the results of the testing and subsequent health outcomes, associated with a disease or health condition. An example of a direct non-medical cost is the cost of transportation to and from a clinic, where said cost is not part the clinic’s charging structure. Anindirect cost comprises one or more costs relating to a productivity loss of an individual or that individual’s family due to a lack of labour force participation because of testing, and management of results of the testing and subsequent health outcomes, associated with a disease or health condition. An example of an indirect cost is the cost associated with a patient or a family member forgoing their usual employment, the latter for caregiving purposes. The healthcare cost may comprise a lifetime healthcare cost based on an average lifetime of an individual at a particular age and gender in region k, or a healthcare cost based on a predetermined period of time. The mortality rate may be based on a life table and an excess mortality associated with a fall and / or fracture in region k. The mortality rate may be based on age.

[0075] The plurality of characteristics relating to falls and fractures Ck may further comprise one or more of: a health benefit measure of individuals in region k and a willingness to pay (WTP) measure for health benefits in region k. The health benefit measure may be a non-monetary measure. It may comprise a given state of health by the years lived in that state. The health benefit measure may be derived from clinical trials, wellbeing studies and / or focus group discussions. The non-monetary measure may include one or more of a quality-of-life measure and a disability measure. The quality-of-life measure may be provided herein as a utility value. The quality-of-life measure and / or disability measure may be based on a fall and / or fracture. The quality-of-life measure and / or disability measure may be based on age. The WTP measure may be a willingness to pay measure for health benefits in general in region kand which may not be specific to fall and / or fractures, however such WTP measure would be taken to relate to falls and fractures in region k in the context of the present application. The WTP measure may be a threshold.

[0076] The plurality of characteristics Ti of each intervention option I may comprise an intervention cost and an intervention effectiveness measure based on the effectiveness of each intervention option / to reduce an incidence of falls and / or an incidence of fractures of individuals in region k. The intervention cost may comprise a lifetime intervention cost based on an average lifetime of an individual at a particular age and gender in region k, or an intervention cost based on a predetermined period of time. The intervention effectiveness measure may be based on data from one or more clinical trials, which include but is not limited to randomized controlled clinical trials.

[0077] In an example, the comparison value V is based on: a function of a health benefit associated with each intervention option I, and a function of a cost associated with each intervention option / , wherein the function of a health benefit and the function of a cost associated with eachintervention option / is based on Ck and Ti. The comparison value V may be based on a predetermined period of time. In an example, the predetermined period comprises a period of 5 cycles where each cycle comprises a year. In some examples, the predetermined period comprises 5 or less cycles, or 5 or more cycles. In some examples, the predetermined period may be up to an individual’s lifespan or an average lifespan of an individual in region k.

[0078] The comparison value V may be determined by equation (1):V = l - Y - Z (1) whereY provides the function of a health benefit associated with each intervention option I and comprises one or more measurable health outcomes associated with each intervention option I in region k, the one or more measurable health outcomes accounting for health state desirability and survival;A comprises a willingness to pay (WTP) measure per unit of health benefit in region k\ andZ provides the function of a cost associated with each intervention option I and comprises at least a healthcare cost and an intervention cost associated with each intervention option / .

[0079] In some examples, the method further comprises subtracting a common comparison value V of one intervention option from each of the comparison values V of the plurality of intervention options. For example, where one of the intervention options of the third data set comprises a “no treatment” option, the method comprises subtracting the comparison value V of the “no treatment” option from each of the comparison values V of the plurality of intervention options to arrive at a secondary comparison value Vs. The secondary comparison values Vsof the plurality of intervention options may be used in the same manner as comparison value V to determine an optimal intervention option. For example, optimal intervention option may be determined based on a maximum secondary comparison value Vs. Unless secondary comparison value Vsis specifically referred to in the present disclosure, any reference to comparison value V includes reference to secondary comparison value Vs.

[0080] The one or more measurable health outcomes accounting for health state desirability and survival may be Quality Adjusted Life Years (QALY), Disability Adjusted Life Years (DALY), hip fractures averted, satisfaction with health services or any measure that is the policy metric in region k. The QALY may be a function of a quality-of-life measure of individuals in region k, andthe amount of time that individuals in region k live in various states of health. A may comprise a WTP measure for an acceptable trade-off threshold of costs per unit of health benefit in region k. The acceptable trade-off threshold may depend on characteristics of region k. A common currency may be applied in the determination of any costs described herein. Equation (1) may be provided by Equation (2) that is used to determine a Net Monetary Benefit (NMB) value as provided by Example 1.

[0081] The plurality of intervention options of the third data set may comprise two or more of: (i) no treatment option, (ii) a bisphosphonate only option, (iii) a falls prevention program option, and (iv) a both bisphosphonate and falls prevention program option. Examples of bisphosphonates include alendronate, risedronate, ibandronate, zoledronic acid and pamidronate.

[0082] The comparison value V may be determined based on one or more of a Markov cohort model, a simple decision tree, a discrete event simulation, a system dynamics simulation, an agent-based model, a statistical fitting and an expert panel-based method. In an example, the comparison value V may be determined based on a Markov cohort model. The one or more aforementioned methods for determining comparison value V may be implemented in a software program such as TreeAge Pro 2021, R1 (TreeAge Software, 2021) and / or any other analysis software program.

[0083] In an example, the method comprises evaluating a plurality of intervention options for falls and / or fractures in an individual, and wherein the first data set comprises a fall probability of the individual and Pfall_i comprises the fall probability of the individual, and the second data set comprises a fracture probability of the Individual and Pfracture_ coimprises the fracture probability of the individual.Pfall_i may be determined by one or more a questionnaire-based falls risk screener, functional assessments (for example, short physical performance battery, timed-up-and-go, 5 x sit to stand and 2 to 5 mins walk), a gait assessment using wearable sensors and clinical risk factors (for example reduced activity of daily living (e.g., as measured by the Barthel Index), comorbidity (e.g., as measured by the Charleson Index) and fear of falling). Pictured may be determined by one or more of an assessment of clinical risk factors (for example age, history of maternal hip fractures, increase in weight since age 25, self-rated health, use of long-acting benzodiazepines, previous hyperthyroidism, current caffeine intake, walking for exercise, inability to rise from chair, resting pulse rate >80 beats / min, any fracture since age of 50 and calcaneal bone density) and imaging (for example Dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI)).

[0084] In an example, the method comprises evaluating a plurality of intervention options forfalls and / or fractures in a population, and wherein the first data set comprises a plurality of fall probabilities, and the second data set comprises a plurality of fracture probabilities, and wherein the method further comprises generating a results data set comprising a plurality of the optimal intervention option determined at eachAs disclosed in the present disclosure, a population comprises a population of individuals, the first data set comprises a plurality of fall probabilities of individuals in the population and the second data set comprises a plurality of fracture probabilities of individuals in the population.

[0085] The method may comprise plottingthree-dimensional plot. The three-dimensional plot may be provided as an output to an output device.

[0086] The method may comprise plottingand the optimal intervention optionas a two-dimensional plot based on Vmax. The two-dimensional plot may be provided as an output to an output device. In some examples, same optimal intervention options at differentare represented with a common identifier. The common identifier may be a colour or tone. In some examples, the same optimal intervention options at differentare provided as a zone on the two-dimensional plot, the two-dimensional plot comprising one or more zones, each representing an optimal intervention option. The two- dimensional plot may comprise a plurality of zones and adjacent zones are separated by a line of equivalence, wherein the line of equivalence provides that the optimal intervention options in the adjacent zones have substantially equivalent effect at a particularandOne or more lines of equivalence may be related to the effectiveness of intervention options on either side of the one or more lines of equivalence, to reduce an incidence of falls and / or an incidence of individual in regionA change (e.g. an improvement) in the effectiveness of one or more intervention options may change the one or more lines of equivalence, hence changes to the one or more lines of equivalence may provide an indication and / or a measure of an impact resulting from a change in the effectiveness of one or more intervention options.

[0087] The method may further comprise receiving an individual’s fall and fracture probabilities based on a fall risk assessment and / or a fracture risk assessment, wherein the fall risk assessment comprises one or more of a questionnaire-based falls risk screener, a functional assessment and a gait assessment, and the fracture risk assessment comprises one or more of clinical risk factors assessment and fracture risk imaging.

[0088] The method may further comprise referencing the results data set with the individual’s fall and fracture probabilities to determine the optimal intervention option for the individual.

[0089] The method may further comprise computing an expected value E(V) based on twoor more comparison values V of the plurality of intervention options of the third data set. Expected value E(V) is the value of the plurality of outcomes for each, weighted by their probability of occurrence; the result is an expected value as the true outcome is unknown in the absence of certainty about risks and benefits and are represented by probability distributions. For example, the method may comprise determining an expected value E(V) based on an average comparison value V generated from a set of falls and fracture probabilities in a region for one or a selected set of intervention options. The average may be a weighted average. The method may further comprise using the expected value E(V) to evaluate one or more falls and fractures risk screening tools and / or evaluating one or more intervention options. The screening tool and / or intervention option that is evaluated may be a new screening tool and / or a new intervention option, and which was not previously evaluated in an embodiment of the present disclosure. The new screening tool or new intervention option may be new to region k, i.e. not yet implemented in the healthcare system of region k. In an example, the expected value E(V) of a first or first set of intervention options may be compared against the expected value E(V) of a second or second set of intervention options and the difference between the expected values E(V) may provide a means to assess whether the cost of research to gain sufficiently accurate estimates of a new screening tool and / or intervention option is worth pursuing in region k. In another example, the method may comprise estimating the maximum value of research for a new screening tool by assuming that the screening tool is perfect, while all other factors remain imperfectly known. The result of this expected value E(V) calculation is the expected value of perfect information (EVPI). Together with the number of individuals likely to be candidates for the new screening tool (N) one can calculate the maximal amount of funding that could be justified on an NMB basis, EVPI x N. This approach applies to estimating the value of imperfect (sample) information (EVSI) such as would be obtained from a clinical study with a finite sample, and to the estimation of the value of screening tools and new intervention options alone or in combination. Expected value E(V) may accordingly comprise the Expected Value of Perfect Information (EVPI) as provided by Example 1 .

[0090] Another aspect of the present disclosure relates to a system for evaluating a plurality of intervention options for falls and / or fractures in individuals. The system comprises a processor configured to receive: a first data set comprising at least one fall probability; a second data set comprising at least one fracture probability; a third data set comprising the plurality of intervention options for falls and / or fractures in individuals, the processor configured to compute a comparison value l / for each interventionoption I of the plurality of intervention options of the third data set, wherein the comparison value Vis determined by the following expression:where Pfall_i comprises a given fall probability of the first data set; Pfracture_icomprises a given fracture probability of the second data set;Ck comprises a plurality of characteristics relating to falls and fractures in a region k;Ti comprises a plurality of characteristics of each intervention option I of the third data set, wherein the processor is configured to determine an optimal intervention option based on the comparison value V of each intervention option I at Pfall_i and Pfracture-_i

[0091] In an example, the processor is configured to determine the optimal intervention option based on a maximum comparison value Vmax. The maximum comparison value Vmaxis taken to be the greatest (or highest) value among the plurality of intervention options at a Ptattj and Pfracture-_ Vima>< may be a positive or negative value.

[0092] One or more of the plurality of intervention options may be an intervention option available in region k. One or more of the plurality of intervention options may be an intervention option that is unavailable in region k but may be considered for region k.

[0093] The fracture probability in the second data set may be one or more of a hip fracture probability, vertebrae fracture probability, wrist fracture probability or other fracture probability. In an example, the second data set comprises at least one hip fracture probability and Pfracture_i comprises a given hip fracture probability.

[0094] The plurality of characteristics relating to falls and fractures Ck may comprise two or more of: an incidence of falls and an incidence of fractures of individuals in region k, a healthcare cost associated with treating an individual in region k for a fall and / or a fracture and a mortality rate associated with a fall and / or fracture of individuals in region k. The incidence of falls and the incidence of fractures may be based on data from epidemiological studies. The data from epidemiological studies is based on age and / or gender. The healthcare cost may comprise oneor more of a direct medical cost, a direct non-medical cost and an indirect cost, as defined elsewhere in the present disclosure. The healthcare cost may comprise a lifetime healthcare cost based on an average lifetime of an individual at a particular age and gender in region k, or a healthcare cost based on a predetermined period of time. The mortality rate may be based on a life table and an excess mortality associated with a fall and / or fracture in region k. The mortality rate may be based on age.

[0095] The plurality of characteristics relating to falls and fractures Ckfurther comprises one or more of: a health benefit measure of individuals in region k and a willingness to pay (WTP) measure for health benefits in region k. The health benefit measure may be a non-monetary measure. It may comprise a given state of health by the years lived in that state. The health benefit measure may be derived from clinical trials, wellbeing studies and / or focus group discussions. The non-monetary measure may include one or more of a quality-of-life measure and a disability measure. The quality-of-life measure may be provided herein as a utility value. The quality-of-life measure and / or disability measure may be based on a fall and / or fracture. The quality-of-life measure and / or disability measure may be based on age. The WTP measure may be a willingness to pay measure for health benefits in general in region k and which may not be specific to fall and / or fractures, however such WTP measure would be taken to relate to falls and fractures in region k in the context of the present application. The WTP measure may be a threshold.

[0096] The plurality of characteristics Ti of each intervention option I may comprise an intervention cost and an intervention effectiveness measure based on the effectiveness of each intervention option / to reduce an incidence of falls and / or an incidence of fractures of individuals in region k. The intervention cost may comprise a lifetime intervention cost based on an average lifetime of an individual at a particular age and gender in region k, or an intervention cost based on a predetermined period of time. The intervention effectiveness measure may be based on data from one or more clinical trials, which include but is not limited to randomized controlled clinical trials.

[0097] In an example, the comparison value V is based on: a function of a health benefit associated with each intervention option / , and a function of a cost associated with each intervention option / , wherein the function of a health benefit and the function of a cost associated with each intervention option I is based on Ckand Ti. The comparison value V may be based on a predetermined period of time. In an example, the predetermined period comprises a period of 5 cycles where each cycle comprises a year. In some examples, the predetermined periodcomprises 5 or less cycles, or 5 or more cycles. In some examples, the predetermined period may be up to an individual’s lifespan or an average lifespan of an individual in region k.

[0098] The processor may be configured to determine the comparison value V based on equation (1):V = k - Y - Z (1) whereY provides the function of a health benefit associated with each intervention option I and comprises one or more measurable health outcomes associated with each intervention option I in region k, the one or more measurable health outcomes accounting for health state desirability and survival;A comprises a willingness to pay (WTP) measure per unit of health benefit in region k; andZ provides the function of a cost associated with each intervention option I and comprises at least a healthcare cost and an intervention cost associated with each intervention option / .

[0099] In some examples, the processor is configured to subtract a common comparison value V of one intervention option from each of the comparison values V of the plurality of intervention options. For example, where one of the intervention options of the third data set comprises a “no treatment” option, the processor is configured to subtract the comparison value V of the “no treatment” option from each of the comparison values Vof the plurality of intervention options to arrive at a secondary comparison value Vs. The secondary comparison values Vsof the plurality of intervention options may be used by the processor in the same manner as comparison value V to determine an optimal intervention option. For example, the processor may be configured to determine the optimal intervention option based on a maximum secondary comparison value Vs.

[0100] The one or more measurable health outcomes accounting for health state desirability and survival may be Quality Adjusted Life Years (QALY), Disability Adjusted Life Years (DALY), hip fractures averted, satisfaction with health services or any measure that is the policy metric in region k. The QALY may be a function of a quality-of-life measure of individuals in region k, and the amount of time that individuals in region k live in various states of health. A may comprise a WTP measure for an acceptable trade-off threshold of costs per unit of health benefit in region k. The acceptable trade-off threshold may depend on characteristics of region k. A commoncurrency may be applied in the determination of any costs described herein. Equation (1) may be provided by Equation (2) that is used to determine a Net Monetary Benefit (NMB) value as provided by Example 1.

[0101] The plurality of intervention options of the third data set may comprise two or more of: (i) no treatment option, (II) a bisphosphonate only option, (Hi) a falls prevention program option, and (iv) a both bisphosphonate and falls prevention program option. Examples of bisphosphonates include alendronate, risedronate, ibandronate, zoledronic acid and pamidronate.

[0102] The processor may be configured to determine the comparison value based on one or more of a Markov cohort model, a simple decision tree, a discrete event simulation, a system dynamics simulation, an agent based model, a statistical fitting and an expert panel-based method. In an example, the processor is configured to determine the comparison value based on a Markov cohort model. The one or more aforementioned methods for determining comparison value may be implemented in a software program such as TreeAge Pro 2021, R1 (TreeAge Software, 2021) and / or any other analysis software program.

[0103] In an example, the system is configured to evaluate a plurality of intervention options for falls and / or fractures in an individual, and wherein the first data set comprises a fall probability of the individual andcomprises the fall probability of the individual, and the second data set comprises a fracture probability of the individual andcomprises the fracture probability of the individual.may be determined by one or more a questionnaire-based falls risk screener, functional assessments (for example, short physical performance battery, timed-up-and-go, 5 x sit to stand and 2 to 5 mlns walk), a gait assessment using wearable sensors and clinical risk factors (for example reduced activity of dally living (e.g., as measured by the Barthel Index), comorbidity (e.g., as measured by the Charleson Index) and fear of falling)..may be determined by one or more of an assessment of clinical risk factors (for example age, history of maternal hip fractures, increase in weight since age 25, self-rated health, use of long-acting benzodiazepines, previous hyperthyroidism, current caffeine intake, walking for exercise, inability to rise from chair, resting pulse rate >80 beats / min, any fracture since age of 50 and calcaneal bone density) and imaging (for example Dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI)).

[0104] In an example, the system is configured to evaluate a plurality of intervention options for falls and / or fractures in a population, and wherein the first data set comprises a plurality of fall probabilities, and the second data set comprises a plurality of fracture probabilities, and wherein the processor is configured to generate a results data set comprising a plurality of the optimalintervention option determined at each

[0105] The processor may be configured to plotand Vma as a three- dimensional plot. The processor may be configured to output the three-dimensional plot to an output device.

[0106] The processor may be configured to plotand the optimal intervention option atas a two-dimensional plot based on Vmax. The processor may be configured to output the two-dimensional plot to an output device. In some examples, the processor is configured to present same optimal intervention options at differentand / or with a common identifier. The common identifier may be a colour or tone. In some examples, the processor is configured to provide the same optimal intervention options at differentas a zone on the two-dimensional plot, the two-dimensional plot comprising one or more zones, each representing an optimal Intervention option. The two-dimensional plot may comprise a plurality of zones and adjacent zones are separated by a line of equivalence, wherein the line of equivalence provides that the optimal intervention options in the adjacent zones have substantially equivalent effect at a particular P^H andOne or more lines of equivalence may be related to the effectiveness of intervention options on either side of the one or more lines of equivalence, to reduce an incidence of falls and / or an incidence of individual in region A change (e.g. an improvement) in the effectiveness of one or more intervention options may change the one or more lines of equivalence, hence changes to the one or more lines of equivalence may provide an indication and / or a measure of an impact resulting from a change in the effectiveness of one or more intervention options.

[0107] The processor may be configured to receive an individual's fall and fracture probabilities based on a fall risk assessment and / or a fracture risk assessment, wherein the fall risk assessment comprises one or more of a questionnaire-based falls risk screener, a functional assessment and a gait assessment, and the fracture risk assessment comprises one or more of clinical risk factors assessment and fracture risk imaging.

[0108] The processor may be configured to reference the results data set with the individual’s fall and fracture probabilities to determine the optimal intervention option for the individual.

[0109] The processor may be configured to compute an expected value E(V) based on two or more comparison values V of the plurality of intervention options of the third data set. Expected value E(V) is the value of the plurality of outcomes for each, weighted by their probability of occurrence; the result is an expected value as the true outcome is unknown in the absence of certainty about risks and benefits and are represented by probability distributions. For example,the processor may be configured to determine an expected value E(V) based on an average comparison value V generated from a set of falls and fracture probabilities in a region for one or a selected set of intervention options. The average may be a weighted average. The processor may be further configured to evaluate one or more falls and fractures risk screening tools and / or one or more intervention options based on expected value E(V). The screening tool and / or intervention option that is evaluated may be a new screening tool or a new intervention option, and which was not previously evaluated in an embodiment of the present disclosure. The new screening tool or new intervention option may be new to region k, i.e. not yet implemented in the healthcare system of region k. In an example, the expected value E(V) of a first or first set of intervention options may be compared against the expected value E(V) of a second or second set of intervention options and the difference between the expected values E(V) may provide a means to assess whether the cost of research to gain sufficiently accurate estimates of a new screening tool and / or intervention option is worth pursuing in region k. In another example, the processor may be configured to estimate the maximum value of research for a new screening tool by assuming that the screening tool is perfect, while all other factors remain imperfectly known. The result of this expected value E(V) calculation is the expected value of perfect information (EVPI). Together with the number of individuals likely to be candidates for the new screening tool (N) one can calculate the maximal amount of funding that could be justified on an NMB basis, EVPI x N. This approach applies to estimating the value of imperfect (sample) information (EVSI) such as would be obtained from a clinical study with a finite sample, and to the estimation of the value of screening tools and new intervention options alone or in combination. Expected value E(V) may accordingly comprise the Expected Value of Perfect Information (EVPI) as provided by Example 1.

[0110] The system may comprise an input device, and the processor may be configured to receive the first and / or second data set from the input device. The input device may comprise one or more of a user interface, storage medium, a sensor and an imaging device. The imaging device may be provided by an imaging machine which may be a Dual-energy X-ray absorptiometry (DXA) scanner. The sensor may comprise a plurality of sensors. The sensor may comprise a wearable sensor, optionally a gait wearable sensor.

[0111] The system may comprise an output device, and the processor is configured to output the optimal intervention option to the output device. The output device may comprise a user interface which may be the same as or separate from the user interface of the input device. The user interface may comprise a display device. The display device may therefore display the optimal intervention option for an individual to a user (e.g. a medical professional). The display device may optionally display the individual’s fall and / or fracture probabilities against one or moreplots described in the present disclosure. In an example, the display device may provide a ranking of the intervention options at the individual’s fall and / or fracture probabilities. The display device may also display one or more action options for a user to select, where the one or more action options relate to an action associated with the displayed optimal intervention option. Such actions may include sending a bisphosphonate prescription to a pharmacy conditional on the approval of a required medical provider, and sending registration details of an individual to a falls prevention program. Upon selection of an action option, the processor is configured to send an action option output to an appropriate recipient (includes a device). For example, an action option for sending a bisphosphonate prescription to a pharmacy for the medication to be retrieved by an individual or delivered to an individual may be displayed for selection, when the optimal intervention option is a bisphosphonate only option or both a bisphosphonate and falls prevention program option. Other action options not related to the optimal intervention option may also be displayed for selection by the user, for example, an action option to send a bisphosphonate prescription to a pharmacy may be displayed even when the optimal intervention option is a falls prevention program only option. Action options not related to the optimal intervention option may be displayed in a different form from action options that relate to the optimal intervention option. Alternatively, the processor may be configured to automatically send an action option output to an appropriate recipient (includes a device) based on the optimal intervention option, for example, a bisphosphonate prescription may be automatically sent by the processor to a pharmacy when the optimal intervention option is a bisphosphonate only option or both a bisphosphonate and falls prevention program option.

[0112] The system may be integrated into a single device. In an example, the imaging machine may comprise one or more components of the system. In an example, the imaging machine comprises the entire system, and in such an example, the imaging machine may comprise at least an electromagnetic radiation transmitter and detector, the processor of the system and a display. The imaging machine may also comprise a movable platform for supporting an individual or a portion of the individual. Alternatively, the system may be formed by multiple devices and one or more components of the system is provided separately in the multiple devices.

[0113] Components of the system may be communicatively coupled together, for example by wired, wireless or other communications media. The system components may communicate with each other through electronic control signals by which one component may direct or control another.

[0114] Another aspect of the present disclosure relates to a non-transitory computer- readable medium having computer executable instructions stored thereon, that when executed by a processor or on a device, cause the processor or the device to perform any method of thepresent disclosure for evaluating a plurality of intervention options for falls and / or fractures in individuals, wherein the method comprises: receiving a first data set comprising at least one fall probability; receiving a second data set comprising at least one fracture probability; receiving a third data set comprising the plurality of intervention options for falls and / or fractures in individuals; computing a comparison value V for each intervention option I of the plurality of intervention options of the third data set, wherein the comparison value Vis determined by the following expression:where Pfall_i comprises a given fall probability of the first data set; Pfracture_i coimprises a given fracture probability of the second data set;Ck comprises a plurality of characteristics relating to falls and fractures in a region k;Ti comprises a plurality of characteristics of each intervention option I of the third data set; and determining an optimal intervention option based on the comparison value Vof each intervention option I at Pfall_i and Pfracture_i

[0115] Features of the method performed by processor or device as caused by the non- transitory computer-readable medium are the same as the features of any method of the present disclosure.

[0116] Embodiments of the present disclosure considers both falls and fracture probabilities of individuals in the evaluation of intervention options and this is an advantage over existing methods and systems which only consider fracture probabilities. Embodiments of the present disclosure allow for a plurality of intervention options (for example three or more intervention options) to be evaluated against each other compared to existing tools which typically only consider one intervention option vs a ‘no action’ option. Embodiments of the present disclosuretherefore provide a more clinically meaningful and accurate evaluation of intervention options, and in particular their cost-effectiveness against both fall and fracture probabilities. For an individual, this may provide a more suited and optimal intervention option given the individuals' fall and fracture probabilities. Additionally, the embodiments of the present disclosure may be tailored to a particular region k based on the characteristics of that region and characteristics of the intervention options being evaluated, and / or be improved based on new information or data of the characteristics of the region and / or characteristics of the intervention options being evaluated. For example, embodiments of the present disclosure may be easily updated based on new information, including new data on test performance (e.g., sensitivity and specificity), new epidemiological data on general pre-test risk probabilities, as well as data on how rapidly individuals who at one test is in one of the various Vmax zones may change from one zone to another zone overtime. The latter information can be useful in the identification of an optimal time for future re-testing and / or evaluation for falls and / or fractures. Embodiments of the present disclosure may also evaluate any new screening tools and / or intervention options as to their effectiveness and economic viability for widespread implementation within a region k. These advantages allow alignment of interventions with evolving characteristics of a region to facilitate proactive and personalised care to a population in the region.EXAMPLESExample 1Methods

[0117] Model Structure

[0118] A Markov model was developed and analyzed using TreeAge Pro 2021 , R1 (TreeAge Software, 2021) to calculate the net monetary benefits over a range of falls and hip fractures probabilities of Singaporean older adults. This model considered falls and fracture incidences, the effects of treatment and mortality over a 5-year period to accumulate health and cost outcomes associated with falls and hip fractures. There are 2 states in the Markov model: alive and dead. T racker variables were used to track previous falls and fractures in an individual. Individuals who had a fall or fracture in a previous cycle had a higher risk of falls or fracture in the subsequent cycles. Four different treatment options (also referred to as intervention option in the present disclosure) were modelled: (1) Do nothing, (2) Bisphosphonates only, (3) Falls prevention program and (4) Both bisphosphonates and Falls prevention program. The term “treatment option” used in Example 1 is taken to be the same as an intervention option referred to elsewhere in the present disclosure and said term includes a “no treatment” option. The model structure is shown in [Fig. 3]. The Markov models track the costs and disutilities for each fall and fractureevent.

[0119] Transitions between the four states “No falls”, “Post falls”, “Post fractures”, “Post falls and fractures” and “Dead” are summarized in the state transition diagram in [FIG. 4]. Post falls / fractures states account for long-term disutilities and costs associated with a fall and fracture, and the increased probability of subsequent falls and fractures.

[0120] A short time horizon of 5 cycles, with a cycle length of 1 year, was chosen to model only the short-term effects of treatment. The long-term health and economic outcomes attributable to the effectiveness of bisphosphonates and falls prevention programs are much less reliable after a period of 5 years from the time of treatment.

[0121] Model Inputs

[0122] Input parameters (CR and Ti) for the Markov model are summarized in [Table 1],

[0123] [Table 1]: Input model parameters for fracture probabilities, mortality rates, utility values and costs.

[0124] Risk of falls and fractures

[0125] A previous fall would increase the likelihood of a future fall. The inventors estimated the relative risk of a future fall based on a history of a previous fall from epidemiological studies on falls in Singapore. Similarly, a previous hip fracture increases the likelihood of a hip fracture in the same hip. The inventors applied estimates of an increased fracture risk based on previously collected empirical data on hip fracture incidences.

[0126] Treatment effectiveness

[0127] The inventors estimated the effectiveness of treatment to reduce falls and fracture risk based on evidence from randomized controlled trials on the effects of alendronate and a community-based falls prevention program. The reduced probability of falls and fractures were applied for the entire 5-year duration modelled.

[0128] Mortality rates

[0129] Acute mortality directly attributable to hip fractures were modelled after each fracture event. The long-term excess mortality following a fracture was also estimated.

[0130] Costs

[0131] This includes healthcare cost and treatment costs. The model adopted the healthcare payer perspective as recommended by the Singapore Agency for Care Effectiveness guidelines for health economics. All costs were in Singapore dollars SGD, and future costs and health benefits were adjusted at a rate of 3% for the time preference for outcomes (both for money and, by a logical extension, to health outcomes; this discounting is separate from inflation and reflects the desirability of economic and health benefits today over such benefits in the future). Direct costs associated with hip fractures were obtained based on studies on osteoporotic fractures in Singapore. Direct costs associated with a fall were derived from a prior economic evaluation of Steps to Avoid Falls in the Elderly (SAFE) falls intervention program. SAFE is a multi-component multi domain group-based falls intervention program that includes both exercise and educational components previously conducted in Singapore.

[0132] Utility values

[0133] Health outcomes were expressed as Quality Adjusted Life Years where the number of years of life was adjusted for quality factors measured by EQ-5D index scores. Hip fractures resulted in a temporary loss of utility, both as an immediate “toll” and in post-fracture health states.

[0134] Cost-Benefit Analyses

[0135] The net monetary benefit (NMB) for a range of plausible values of probabilities for falls and fractures were plotted in a three-dimensional plot. NMB is defined by equation (2) as follows:NMB = A ■ Benefit - Cost (2)

[0136] Cost is the difference in cost of each treatment option vs. no treatment (doing nothing), Benefit is the difference in QALY for each treatment option vs. no treatment (doing nothing), and A is the threshold value for WTP for health benefits expressed by SGD per additional QALY.

[0137] At a given fixed probability of fall and fracture, the NMB for each treatment option is determined and an example is shown in [Table 2],

[0138] [Table 2]: Examples of NMB values at a given fall and fracture probability for each treatment option.

[0139] The maximum NMB at each probability of fall and fracture and the associated treatment option is then used to colour a plot to highlight the boundaries between each treatment option.

[0140] Simulation Cohorts to estimate maximum NMB

[0141] In the deterministic model, the inventors assumed that they have complete certainty over all the parameters in the model. This assumption allows them to estimate the maximum willingness to pay to get perfect information on all the parameters within the model. An estimation of this threshold is known as the Expected Value of Perfect Information (EVPI). The EVPI can then be referenced as a threshold at which the cost of future research to gain certainty over estimates of the probability of falls and fractures for Singaporean older adults should not exceed. This is valuable to estimate the potential benefit of future research efforts into falls and fractures risk screening efforts.

[0142] Estimating EVPI requires a simulation of the natural spread of falls and fractures risk in a cohort based on Singaporean older adults. The time to a fall or a fracture was simulated using a gamma distribution parameterized using falls and fractures incidence rates from existing literature. The probabilities of falls and fractures for 10 000 individuals were calculated by obtaining the probability that the time to a fall or fracture was less than 1 year. Four different cohorts were simulated: a cohort with mean falls and fractures incidence corresponding to (1) older adults at the age of 50 years old, and (2) 60 years old, (3) 70 years old and (4) 80 years old. These simulated probabilities are then used to evaluate the differences in average NMB for individuals in the cohort between only having bisphosphonates as an intervention option and having both bisphosphonates and falls prevention programs as the most optimal treatment, with the decision on the intervention option made based on perfect information from falls and fractures risk predictions. The average NMB gained between the two treatment decisions is the EVPI. This application is illustrative and can be extended to accommodate the level of certainty NMB of a previously optimal strategy and any new intervention option (i.e., Expected Value of Sample Information (EVSI)).

[0143] Sensitivity Analyses

[0144] In the calculations of net monetary benefit, three different willingness-to-pay thresholds were used to demonstrate the robustness of the inventors' findings. A threshold of SGD$0 to demonstrate only cost-saving treatment decisions, a threshold of SGD$ 45 000 and a threshold of SGD$ 75 000 as recommended by the Agency for Cost Effectiveness (ACE) Singapore to facilitate policy decision making. This threshold can be modified, including willingness-to-pay threshold = 0 (indicating which of various strategies would be cost minimizing), and for which economic considerations are not to be considered (i.e., results only provided for relative expected health benefits). The analysis can also accommodate the need in a particular region k for a range of discount rates to account for time preferences for future vs. present costand health benefits.Results

[0145] Sensitivity analyses using extreme values for model inputs are consistent with a priori expectations of the results in response to the changes.

[0146] Four different three-dimensional plots for the range of NMB over the full range of probabilities of falls and fractures are shown in [Fig. 5] for (a) doing nothing, (b) bisphosphonates only, (c) falls prevention program only, and (d) both bisphosphonates and falls prevention programs. [Fig. 6] illustrates these three-dimensional plots as two-dimensional plots.

[0147] The NMB when the probability of falls and fractures are both 0 at a WTP threshold of SGD$ 75 000 are (1) SGD$ 326 280, (2) SGD$325 280, (3) SGD$ 321 815 and (4) SGD$ 320 816 per person over 5 years for (a) do nothing, (b) bisphosphonates only, (c) falls prevention program only, and (d) both bisphosphonates and falls prevention program respectively. The NMB when the probability of falls and fractures are both 1 at a WTP threshold of SGD$ 75 000 are (1) SGD$ 17434, (2) SGD$48 523, (3) SGD$ 65 127 and (4) SGD$ 98 066 per person over 5 years respectively.

[0148] In all four plots, the lowest NMB is achieved when the probability of a fall and fracture is 1 , and highest when the probability of the fall and fracture is 0. This is due to the significant cost and disutility associated with a fall and fracture, relative to the cost and disutility of treatment. NMB increases mostly symmetrically from probability of 1 of falls and fractures to probability of 0 of falls and fractures. In the “bisphosphonates only” plot, a higher NMB is achieved at a higher probability of a fracture compared to a similar probability of a fall. Similarly, in the “falls prevention program only” plot, a higher NMB is achieved at a higher probability of a fall, compared to a similar probability of a fracture. The area on the plot where the overall NMB is less than SGD$ 100 000 is largest in the “Do nothing” and “Both bisphosphonates and falls prevention program” plots, and smallest in the “Bisphosphonates only” and “Falls prevention program” only plots. The area with the highest NMB of between SGD$ 300000 to SGD$ 400000 is smallest in the “Do nothing" plot, and largest in the “Both bisphosphonates and falls prevention program" plot.

[0149] The treatment decision that produces the highest NMB at each probability of falls and fractures risk is presented in [Fig. 7]. The analysis was conducted at a WTP threshold of (a) SGD$ 0, (b) SGD$15 000, (c) SGD$45 000 and (d) SGD$ 75 000. The different tones represent the treatment decision at that point with the highest NMB. Same treatment decisions are shown as zones, with each zone having its own tone and the zones are separated by lines of equivalence, where the line of equivalence provides that the optimal treatment decision in the adjacent zoneshave substantially equivalent effect. At a WTP threshold of $0, only cost-saving treatment options are presented, disregarding any potential gains in utility due to reduced mortality, or improved quality of life from avoiding negative health outcomes.

[0150] A summary table of the average probability from the simulation model of falls and fractures probabilities at ages 50, 60, 70 and 80 can be found in [Table 3]. [Table 3] provides a summary of simulation results on NMB across four different ages of Singaporean Older Adults. The calculated average NMB per person over 5 years with a strategy of doing nothing is SGD$ 276 848, SGD$ 257 289, SGD$ 221 218 and SGD$ 190 118 at ages 50, 60, 70 and 80 respectively. The NMB decreases at older ages due to the higher probabilities of both falls and fractures, resulting in poorer health outcomes. The estimated EVPI at ages of 50, 60, 70 and 80 are SGD$ 283 868, SGD$ 276474, SGD$ 250410 and SGD$ 227 752 respectively. This results in a growing difference between doing nothing and taking the most optimal treatment option at each probability of falls and fractures for each age cohort. The differences are SGD$ 16 630, SGD$ 19 185, SGD$ 29 192 and SGD$ 37 634 respectively. For each cohort, the proportion of individuals with “do nothing” as the most optimal treatment option decreases from 85% at age 50 to 68% at age 80.

[0151] [Table 3]: Summary of simulation results on NMB across four different ages of Singaporean Older Adults.Discussion

[0152] Example 1 demonstrates the NMB over a range of probabilities of falls and fractures for four different treatment decisions for (1) doing nothing, (2) bisphosphonates only, (3) falls prevention program only, and (4) both bisphosphonates and falls prevention programs. The results showed that both bisphosphonates and falls prevention programs are both cost-effective interventions to reduce the burden of falls. Falls prevention programs have the potential to be more cost-effective relative to bisphosphonates as they prevent falls, and thus reduce the likelihood of most fractures due to trauma. The combined use of falls prevention programs andbisphosphonates have synergistic effects to reduce the likelihood of a fall-related fractures, resulting in a higher NMB achieved (Hurley & Armstrong, 2012; Zhou et al., 2020). Muscle loading or resistance exercise have been shown to improve BMD (bone mineral density), while falls prevention programs reduce the likelihood of falls-related fractures (Kemmler et aL, 2013; Rodrigues et aL, 2021).

[0153] In the sensitivity analysis, the inventors determined how those boundaries would change when the WTP decreased from SGD$ 70 000 to SGD$ 45 000 and SGD$ 0. Bisphosphonates are the only cost-saving therapeutic option to be considered at a WTP threshold of SGD$ 0. Bisphosphonates have been consistently proven as a cost-effective and clinically effective treatment option to prevent osteoporotic related fractures in older adults. The challenge, however, is to appropriately identify the correct sub-population of individuals to give bisphosphonates to. Previous studies have shown that the population attributable fraction for fractures related to low bone mineral density is only about 40% (Mai et aL, 2019). The model of Example 1 suggests that by considering the probability of a fall through other predictive methods, the selection criteria of patients to put on bisphosphonate treatment can be improved and costsavings maximized from this important and effective treatment.

[0154] At each probability of falls and fractures, conservative estimates of the costs of the treatment of choice are made, and the effectiveness of the use of bisphosphonates and falls prevention programs. While the costs and effectiveness of bisphosphonates have been largely proven in large scale clinical trials, there is greater uncertainty in the effectiveness and cost of a community-based falls prevention program (Hill et aL, 2018; Hurley & Armstrong, 2012). The effectiveness of community-based programs in reducing falls and improving health outcomes vary significantly from a relative risk ratio of 0.56 to 0.77 across studies in Asia (Hill et aL, 2018). The relative risk ratio reported in other studies also depends on the baseline risk of a fall in the older adults participating in the trial, adherence rates of the participants, and the fidelity in the conduct of the program. Furthermore, there are few large-scale clinical trials that rigorously evaluate the effectiveness of community-based falls prevention programs. Thus, there is significant potential for falls prevention programs to achieve greater effectiveness. Furthermore, in most costeffectiveness analyses of falls prevention programs, only the direct healthcare costs of the program are considered (Matchar et aL, 2019). However, there are significant direct non-medical and / or indirect cost savings associated with a reduction in fractures risk that may not have been considered such as productivity effects related to caregiver reducing work time due to caregiving, and to patient loss of functional independence affecting employment. Thus, falls prevention programs should be systematically and strategically implemented to improve health and cost outcomes for older adults.

[0155] Example 1 identified the thresholds of falls and fractures probabilities at which current therapeutic options currently available will be most cost-effective. Maximizing cost-effectiveness, therefore, relies on an accurate prediction of falls and fractures probabilities. Current methods of predicting falls include the use of questionnaire-based regression models, and functional tests (Chen, 2018; Regan et al., 2020; Shumway-Cook et al., 2000). Novel technologies such as wearable gait sensors and other technologies that assess gait parameters also offer a rapid and efficient way of predicting falls risk (Lockhart et al., 2021). Fracture risk prediction is currently done using the Fracture Risk Assessment Tool (FRAX) which relies on Dual X-ray Absorptiometry (DXA) based measurements of bone mineral density, together with clinical risk factors (Chandran et al., 2018; Kanis et al., 2008). A clinical decision tool that only considers bone mineral density or the likelihood of a fall would be limited in its effectiveness in recommending the most appropriate therapeutic option for the patient.

[0156] Using the simulation cohorts of Singaporean older adults in Example 1 , the inventors have showed that the NMB gained from selecting the most appropriate therapeutic option increases with age. The relatively short time horizon of 5 years used in the present evaluations introduces a small bias by not accounting for the reduced mortality due to optimal treatment on life expectancy. Example 1 may underestimate the NMB gained in younger age groups due to the prevention of falls and fractures. However, many studies suggest that bisphosphonates have a relatively short therapeutic window of 5 to 10 years before other anti-bone resorptive therapies should be considered. Studies evaluating the effectiveness of falls prevention programs have limited follow up of up to two years. Therefore, any effectiveness beyond that period due to longterm behavioural change in habits and physical activity out to be extrapolated. The inventors have chosen not to make that extrapolation in Example 1 but this does not mean that such extrapolation cannot be done. Thus, for purposes of illustration, the inventors have used assumptions that are appropriate in estimating the cost-effectiveness of the therapeutic treatment options available.

[0157] The model of Example 1 may be incorporated into a clinical decision tool to help clinicians make the most cost-effective therapeutic option for their patients based on their predicted risk of falls and fractures. For example, one of the plots in Fig. 7 may be used to determine an optimal therapeutic option based on that individual's fall and fracture probabilities. Currently, the methods for predicting falls and fractures to decide therapeutic options (i.e. functional tests for predicting falls risk, and FRAX for predicting fractures risk) have low accuracy and are not consistently applied in the clinical setting (Jiang et al., 2017; Shumway-Cook et aL, 2000). Furthermore, there are significant implementation barriers to encouraging clinicians to refer patients into a community-based falls prevention program (Koh et aL, 2023). However, novel health technologies currently being developed allow for the accurate prediction of falls andfractures risk. Thus, clinicians now require tools that enable them to interpret both falls and fractures risk simultaneously and recommend the most appropriate therapeutic action. The model of Example 1 is therefore capable of improving the cost-effectiveness of clinical practice, and lends more confidence to clinicians to make recommendations to their patients.

[0158] At a policy level, the early assessment of cost-effectiveness allows policymakers to determine the level of investment to fund health services research to predict falls and fractures in older adults. As the population ages, the incidence of falls and fractures in Singapore rises. Despite recent studies that showed that the age-adjusted incidence of fractures decreasing, the projected absolute numbers of fractures in Singaporean older adults will nearly triple by 2050 (Chandran et al., 2019; Wu et al., 2021; Yong et al., 2019). Researchers have long advocated for increased screening of bone mineral density in the older population (French & Emanuele, 2019; US Preventive Services Task Force, 2018). Example 1 therefore, provides an upper limit to the level of investment that government payers should consider spending to obtain more accurate fracture risk estimates, conditional on the size of the population anticipated to be effected by the requisite research.

[0159] Additionally, Example 1 provides a means of evaluating screening tools in terms of their ability to impact treatment decisions. For example, [Fig. 8(a)] shows a pre-test fall and fracture probability of “A”, and a post-test fall and fracture probability of “B” (positive test) and “C” (negative test), of an individual based on results after an exemplary screening tool is used, against a plot of optimal treatment decisions for each probability of falls and fractures in a region k at a WTP threshold of $75 000. The pre-test fall and fracture probability “A” may be a certain fall and fracture probability that the individual is estimated to have based for example on the individual’s age, gender, and epidemiological data in the region k, before the screening tool is used. The posttest fall and fracture probabilities “B” and “C” are the fall and fracture probabilities obtained based on the results after the exemplary screening tool is used. Post-test fall and fracture probability “B” comprises a fall probability that is higher than the fall probability of the pre-test fall and fracture probability “A”, i.e. pre-test fall and fracture probability ”A” underestimated the individual’s fall probability. Post-test fall and fracture probability “C” comprises a fall probability that is lower than the fall probability of the pre-test fall and fracture probability “A”, i.e. pre-test fall and fracture probability ”A” overestimated the individual’s fall probability. The exemplary screening tool used to obtain post-test fall and fracture probability “B” or “C” would be deemed a useful screening tool because a positive test (i.e. “B”) would change the decision on the choice of the intervention option recommended to the individual.

[0160] [Fig. 8(b)] shows a pre-test fall and fracture probability of “D”, and a post-test fall and fracture probability of “E” (positive test) and “F” (negative test) of an individual based on resultsafter another exemplary screening tool (different from that in [Fig. 8(a)]) is used, against a plot of optimal treatment decisions for each probability of falls and fracturs in a region k at a WTP threshold of $75 000. The plots of optimal treatment decisions for each probability of falls and fracturs in a region / rat a WTP threshold of $75000 of Figs. 8(a) and 8(b) are the same. The pretest fall and fracture probability “D” may be a certain fall and fracture probability that the individual is estimated to have based for example on the individual’s age, gender, and epidemiological data in the region k, before the screening tool is used. The post-test fall and fracture probabilities “E” and “F” are the fall and fracture probabilities obtained based on the results after the exemplary screening tool is used. Post-test fall and fracture probability “E” comprises a fall probability that is higher than the fall probability of the pre-test fall and fracture probability “D”, i.e. pre-test fall and fracture probability ”D” underestimated the individual’s fall probability. Post-test fall and fracture probability “F” comprises a fall probability that is lower than the fall probability of the pre-test fall and fracture probability “D”, i.e. pre-test fall and fracture probability ”D” overestimated the individual’s fall probability. In comparison to the exemplary screening tool of [Fig. 8(a)], the exemplary screening tool of [Fig. 8(b)] used to obtain post-test fall and fracture probability “E” or “F” would not be deemed a useful screening tool because neither a positive nor a negative screening test result would change the decision on the choice of the intervention option recommended to the individual.

[0161] Furthermore, the inventors have shown that in addition to getting more accurate fracture risk estimates to guide use of bisphosphonates alone, there is additional cost-benefits to combining the use of falls and fractures risk estimations to make therapeutic decisions on whether to start on bisphosphonates or falls prevention programs. New therapeutic options or modifications to existing therapeutic options which improve their effectiveness may also be evaluated with Example 1 , including intervention options that focus primarily on falls prevention. For example, [Fig. 9(a)] shows a plot of optimal intervention options at each fall probability and fracture probability with a falls prevention program that reduces the rate of falls by 36% and [Fig. 9(b)] shows a plot of optimal intervention options at each fall probability and fracture probability with a falls prevention program that reduces the rate of falls by 60%.

[0162] In Example 1 , the inventors have shown that falls prevention programs play an integral role in driving cost effectiveness of reducing falls burden. These falls prevention programs are effective and cost-effective in reducing fracture related health burdens (Frick et al., 2010; Stevens & Olson, 2000; R. M. Y. Wong et aL, 2020). With the abundance of research into falls prevention and reducing fracture rates, there is an impetus for Singapore to initiate a national level falls prevention strategy to reduce falls and falls related burden in community-dwelling older adults.Example 2

[0163] A clinical decision tool incorporating the model of Example 1 was used to recommend an intervention option to two individuals, G and H, where the characteristics of the region k was that of Singapore, at a willingness to pay threshold of SGD$ 75 000.

[0164] Individual G was male, 71 years of age, 160.6 cm tall and weighs 56.5 kg. Individual G had a fall and fracture probability of 81 % and 1.46% respectively.

[0165] Individual H was female, 78 years of age, 149.1 cm tall and weights 48.2 kg. Individual H had a fall and fracture probability of 85% and 4.53% respectively.

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Claims

1.ClaimsClaim 1. A method of evaluating a plurality of intervention options for falls and / or fractures in individuals, the method comprising: receiving a first data set comprising at least one fall probability; receiving a second data set comprising at least one fracture probability; receiving a third data set comprising the plurality of intervention options for falls and / or fractures in individuals; computing a comparison valuefor each intervention option of the plurality of intervention options of the third data set, wherein the comparison value is determined by the following expression:wherePMU comprises a given fall probability of the first data set;comprises a given fracture probability of the second data set;comprises a plurality of characteristics relating to falls and fractures in a regioncomprises a plurality of characteristics of each intervention option / of the third data set; and determining an optimal intervention option based on the comparison value V of each intervention optionClaim 2. The method of claim 1 , wherein the step of determining an optimal intervention option comprises comparing the comparison values of the plurality of intervention options and determining the optimal intervention option based on a maximum comparison Value Vmax.Claim 3. The method of claim 1 or 2, wherein the second data set comprises at least one hip fracture probability andcomprises a given hip fracture probability.Claim 4. The method of any one of claims 1 to 3, wherein the plurality of characteristics relating to falls and fractures C* comprises two or more of: an incidence of falls and an incidence of fractures of individuals in region k, wherein optionally the incidence of falls and the incidence of fractures are based on data from epidemiological studies, and optionally the data from epidemiological studies is based on age and / or gender; a healthcare cost associated with treating an individual in region kfor a fall and / or a fracture; and a mortality rate associated with a fall and / or fracture of individuals in region k, wherein optionally, the mortality rate is based on a life table and an excess mortality associated with a fall and / or fracture in region k.Claim 5. The method of claim 4, wherein the plurality of characteristics relating to falls and fractures Ck further comprises one or more of: a health benefit measure of individuals in region k, wherein optionally the health benefit measure is a non-monetary measure; and a willingness to pay (WTP) measure for health benefits in region k.Claim 6. The method any one of claims 1 to 5, wherein the plurality of characteristics Ti of each intervention option I comprises: an intervention cost; and an intervention effectiveness measure based on the effectiveness of each intervention option I to reduce an or the incidence of falls and / or an or the incidence of fractures of individuals in region k, wherein optionally the intervention effectiveness measure is based on data from one or more clinical trials.Claim 7. The method of any one of claims 1 to 6, wherein the comparison value V is based on: a function of a health benefit associated with each intervention option I, and a function of a cost associated with each intervention option / , wherein the function of a health benefit and the function of a cost associated with each intervention option I is based on C* and Ti.Claim 8. The method of claim 7, wherein the comparison value V is determined by equation d):V = Z - Y - Z (1) whereY provides the function of a health benefit associated with each intervention option I and comprises one or more measurable health outcomes associated with each intervention option I in region k, the one or more measurable health outcomes accounting for health state desirability and survival;A comprises a or the willingness to pay (WTP) measure per unit of health benefit in region k, andZ provides the function of a cost associated with each intervention option I and comprises at least a or the healthcare cost and a or the intervention cost associated with each intervention option / .Claim 9. The method of any one of claims 1 to 8, wherein the plurality of intervention options of the third data set comprises two or more of: (i) no treatment option, (ii) a bisphosphonate only option, (iii) a falls prevention program option, and (iv) a both bisphosphonate and falls prevention program option.Claim 10. The method ofany one of claims 1 to 9, wherein the comparison value V is determined based on one or more of a Markov cohort model, a simple decision tree, a discrete event simulation, a system dynamics simulation, an agent based model, a statistical fitting and an expert panel-based method, wherein optionally, the comparison value V is determined based on a Markov cohort model.Claim 11 . The method of any one of claims 1 to 10, wherein the method comprises evaluating a plurality of intervention options for falls and / or fractures in an individual, and wherein the first data set comprises a fall probability of the individual and Ptauj comprises the fall probability of the individual; and the second data set comprises a fracture probability of the individual and Pf^tu^j comprises the fracture probability of the individual.Claim 12. The method of any one of claims 1 to 10, wherein the method comprises evaluating a plurality of intervention options for falls and / or fractures in a population, and whereinthe first data set comprises a plurality of fall probabilities; and the second data set comprises a plurality of fracture probabilities, and wherein the method further comprises generating a results data set comprising a plurality of the optimal intervention option determined at eachan Pdfracture_.iClaim 13. The method of claim 12 when dependent on claim 2, wherein the method further comprises plottingj and Vmaxas a three-dimensional plot.Claim 14. The method of claim 12 when dependent on claim 2, wherein the method further comprises plottingand the optimal intervention option atanda two-dimensional plot based on Vmax.Claim 15. The method of claim 14, wherein same optimal intervention options at differentand / orare represented with a common identifier, wherein optionally, the common identifier comprises a colour or tone.Claim 16. The method of claim 15, wherein same optimal intervention options at differentand / orare provided as a zone on the two-dimensional plot, the two- dimensional plot comprising one or more zones, each representing an optimal intervention option.Claim 17. The method of claim 16, wherein the two-dimensional plot comprises a plurality of zones and adjacent zones are separated by a line of equivalence, wherein the line of equivalence provides that the optimal intervention options in the adjacent zones have substantially equivalent effect at a particularClaim 18. The method of any one of claims 12 to 17, the method further comprising receiving an individual’s fall and fracture probabilities based on a fall risk assessment and / or a fracture risk assessment, wherein the fall risk assessment comprises one or more of a questionnaire-based falls risk screener, a functional assessment and a gait assessment, and the fracture risk assessment comprises one or more of clinical risk factors assessment and fracture risk imaging.Claim 19. The method of claim 18, the method further comprising referencing the results data set with the individual’s fall and fracture probabilities to determine the optimal intervention option for the individual.Claim 20. The method of any one of claims 12 to 19, the method further comprising computing an expected value E(V) based on two or more comparison values V of the plurality of intervention options of the third data set, wherein the method comprises using the expected value E(V) to evaluate one or more falls and fractures risk screening tools and / or evaluating one or more new intervention options.Claim 21. A system for evaluating a plurality of intervention options for falls and / or fractures in individuals, the system comprising: a processor configured to receive: a first data set comprising at least one fall probability; a second data set comprising at least one fracture probability; a third data set comprising the plurality of intervention options for falls and / or fractures in individuals, the processor configured to compute a comparison value V for each intervention option I of the plurality of intervention options of the third data set, wherein the comparison value V is determined by the following expression:where Pfall_i comprises a given fall probability of the first data set; Pfracture_i j comprises a given fracture probability of the second data set;Ck comprises a plurality of characteristics relating to falls and fractures in a region k;Ti comprises a plurality of characteristics of each intervention option I of the third data set, wherein the processor is configured to determine an optimal intervention option based on the comparison value V of each intervention option I at Pfall_i and Pfractujre. _iClaim 22. The system of claim 21 , wherein the processor is configured to determine the optimal intervention option based on a maximum comparison value Vmax.Claim 23. The system of claim 21 or 22, wherein the plurality of characteristics relating to falls and fractures C* comprises two or more of: an incidence of falls and an incidence of fractures of individuals in region k, wherein optionally the incidence of falls and the incidence of fractures are based on data from epidemiological studies, and optionally the data from epidemiological studies is based on age and / or gender; a healthcare cost associated with treating an individual in region kfor a fall and / or a fracture; and a mortality rate associated with a fall and / or fracture of individuals in region k, wherein optionally, the mortality rate is based on a life table and an excess mortality associated with a fall and / or fracture in region k.Claim 24. The system of claim 23, wherein the plurality of characteristics relating to falls and fractures Ck further comprises one or more of: a health benefit measure of individuals in region k, wherein optionally the health benefit measure is a non-monetary measure; and a willingness to pay (WTP) measure for health benefits in region k.Claim 25. The system any one of claims 21 to 24, wherein the plurality of characteristics Ti of each intervention option I comprises an intervention cost; and an intervention effectiveness measure based on the effectiveness of each intervention option I to reduce an or the incidence of falls and / or an or the incidence of fractures of individuals in region k, wherein optionally the intervention effectiveness measure is based on data from one or more clinical trials.Claim 26. The system of any one of claims 21 to 25, wherein the comparison value V is based on: a function of a health benefit associated with each intervention option I, and a function of a cost associated with each intervention option / , wherein the function of a health benefit and the function of a cost associated with each intervention option I is based on Ck and Ti.Claim 27. The system of claim 26, wherein the processor is configured to determine the comparison value V based on equation (1):V = - Y - Z (1) whereY provides the function of a health benefit associated with each intervention option I and comprises one or more measurable health outcomes associated with each intervention option I in region k, the one or more measurable health outcomes accounting for health state desirability and survival;A comprises a or the willingness to pay (WTP) measure per unit of health benefit in region , andZ provides the function of a cost associated with each intervention option I and comprises at least a or the healthcare cost and a or the intervention cost associated with each intervention option / .Claim 28. The system of any one of claims 21 to 27, wherein the plurality of intervention options of the third data set comprises two or more of: (i) no treatment option, (ii) a bisphosphonate only option, (iii) a falls prevention program option, and (iv) a both bisphosphonate and falls prevention program option.Claim 29. The system of any one or claims 21 to 28, wherein the system comprises an input device, the processor configured to receive the first and / or second data set from the input device, wherein the input device comprises one or more of a user interface, storage medium, a sensor and an imaging device.Claim 30. The system of any one of claims 21 to 29, wherein the system comprises an output device, the processor configured to output the optimal intervention option to the output device, wherein optionally, the output device comprises a or the user interface.Claim 31. A non-transitory computer-readable medium having computer executable instructions stored thereon, that when executed by a processor or on a device, cause the processor or the device to perform steps comprising: receiving a first data set comprising at least one fall probability; receiving a second data set comprising at least one fracture probability;receiving a third data set comprising the plurality of intervention options for falls and / or fractures in individuals; computing a comparison value V for each intervention option I of the plurality of intervention options of the third data set, wherein the comparison value V is determined by the following expression:where Pfall_i comprises a given fall probability of the first data set; Pfracture_ji comprises a given fracture probability of the second data set;Ck comprises a plurality of characteristics relating to falls and fractures in a region k;Ti comprises a plurality of characteristics of each intervention option I of the third data set; and determining an optimal intervention option based on the comparison value V of each intervention option I at Pfall_i and Pfractujre-_i

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