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Abstract

Background

The Agency for Care Effectiveness of Singapore has advised primary care physicians to use osteoporosis screening tools to risk-stratify patients in the primary care setting.

Objective

This paper aims to report the uptake of a “Predict and Prevent” workflow for osteoporosis using risk scoring and BMD measurement in patients with chronic diseases seen in a network of primary care clinics in Singapore from 2020 to 2021.

Methods

A “Predict and Prevent” osteoporosis preventive care programme was implemented at a network of 11 primary care clinics. The programme included all adult patients that consulted at the clinic for a chronic condition. OSTA score was computed for each patient. All patients who consented to further fracture risk screening underwent FRAX scoring. Female patients with a high-risk OSTA score and females with diabetes with intermediate-risk OSTA score (per protocol), or those with high-risk FRAX score were recommended to undergo BMD measurement.

Results

Of the 6,332 adult patients with chronic diseases without baseline osteoporosis seen in the various clinics, 81.1% underwent OSTA scoring; 28.1% were intermediate risk and 7.0% were high risk. Among the per-protocol population (n = 531), 38 (7.1%) underwent BMD testing. FRAX scoring (without BMD) was done on 939 patients (17.3%); 31.5% had a high hip fracture risk. Only 14 patients had FRAX with BMD; 57.1% had a high hip fracture risk.

Conclusion

A “Predict and Prevent” workflow could be implemented to screen, detect and potentially treat patients at high risk of osteoporosis. The rate of BMD measurement is low and needs to be improved.

Keywords

Bone mineral density osteoporosis screening risk assessment chronic disease

Introduction

According to the International Osteoporosis Foundation Asian Audit, Singapore has the highest reported incidence of hip fractures in Asia.¹ In the said audit, one in every three Singaporean women over the age of 50 years has osteoporosis. Furthermore, around half of women older than 60 years were at intermediate risk of osteoporosis whereas one-quarter were at high risk.

In 2017, the total number of osteoporotic fractures in Singapore was 15,267 – this figure is expected to increase to 24,104 in 2035 (58% increase in less than 20 years) unless drastic interventions are implemented nationwide.² These osteoporotic fractures have been projected to cost SGD 289.6 million in 2035.^2,3

Osteoporosis is also prevalent in patients with chronic diseases such as diabetes, hypertension and dyslipidaemia. This association is due to several reasons, such as the generally advanced age of patients with chronic diseases, metabolic derangements associated with chronic diseases that impair bone homeostasis, and the impact of medications on bone mineral density (BMD).^4–9 Hence, osteoporosis is major health concern among Singapore’s ageing population.

The Agency for Care Effectiveness (ACE) of Singapore has issued guidance on the screening and management of osteoporosis among Singaporean residents.¹⁰ In the said guidance, primary care physicians are advised to use screening tools to risk-stratify patients in the primary care setting and to initiate pharmacological treatment for patients with osteoporosis seen in the primary care setting.¹⁰

Various screening tools for osteoporosis are available. The Osteoporosis Self-Assessment Tool for Asians (OSTA) was developed by the World Health Organisation to identify women at risk of osteoporosis and is based only on two readily available pieces of information: a patient’ s age and weight.¹¹ This score was developed based on data from 860 women from eight Asian countries. ACE has also recommended a revised formula for local use.¹⁰

On the other hand, the Fracture Risk Assessment Tool (FRAX) was developed by the University of Sheffield, UK, to estimate a person’s 10-year probability of a major fracture as well as of a hip fracture. The FRAX score considers the following factors: age (years), weight (kg), height (cm), sex (male vs female), BMD, and the presence or absence of current smoking, history of fractures, parental history of fractures, rheumatoid arthritis, glucocorticoid use, secondary osteoporosis, and daily alcohol intake of three or more units.^12–14 A FRAX score can be calculated with or without BMD measurements.¹⁴

This paper reports the experience and performance of a “Predict and Prevent” osteoporosis preventive care programme conducted among patients with chronic diseases seen in Frontier Healthcare, a network of primary care clinics in Singapore, during the years 2020 to 2021. Specifically, the paper reports the uptake of risk scoring (using OSTA and FRAX scores) and subsequent BMD measurement for those with high risk scores.

Methods

Included patients

The programme included all adult patients (aged 18 years and older) that consulted at the clinic (whether virtually or in-clinic) for a chronic condition (including but not exclusive to diabetes mellitus (DM), hypertension, dyslipidaemia and asthma) under the Chronic Disease Registries, and consented to participate in the programme. Patients with baseline osteoporosis were excluded from the analysis.

Programme design

The osteoporosis preventive care programme was a collaboration between Frontier Healthcare and Amgen Singapore and was implemented in all 11 primary care clinics of Frontier Healthcare. The programme objective was to establish and implement a “Predict and Prevent” workflow for osteoporosis in the primary care setting. This “Predict and Prevent” workflow aims to ensure that osteoporosis screening to systematically included in the preventive care of patients with chronic diseases in the primary care setting. According to institution guidelines, a formal application for ethical approval is not applicable as this study was a quality-improvement project.

The workflow is described in Figure 1. OSTA score was computed for each patient, using in-clinic weight measurements, using the following local formula: OSTA score = weight (kg) minus age (years).¹⁰ An OSTA score of less than 0 was considered low risk for primary osteoporosis; 0 to 20 was intermediate risk; and greater than 20 was high risk. Female patients with a high-risk OSTA score were recommended to undergo BMD measurement. The protocol also recommended BMD measurement for female patients with an intermediate-risk OSTA score based on studies indicating that DM is a risk factor of osteoporosis.^8,9 All patients who consented to further fracture risk screening underwent FRAX scoring to calculate the 10-year probability of a major fracture and a hip fracture. If previous BMD results were available, FRAX score with BMD was calculated; however, given that the BMD is an optional input variable, FRAX score without BMD was calculated in the absence of a previous BMD result.¹⁴ A 10-year probability of at least 20% for major fractures or at least 3% for hip fractures was considered high risk. Patients with a high-risk FRAX score were recommended to undergo BMD measurement.

Figure 1.

“Predict and Prevent” workflow. BMD, bone mass density; FRAX, fracture risk assessment tool; OSTA, osteoporosis self-assessment tool for Asians.

Data extraction and analysis

Data from patient records were extracted from patient records and anonymised by removing direct identifiers. The deidentified data was then transferred to a confidential database accessible only to the chief medical officer and the statistician. Missing data were not imputed.

The performance and outcomes of the programme were reported based on the number of patients with OSTA scores and FRAX scores over the number of eligible patients, number and proportion of patients in each OSTA and FRAX score (with or without BMD) risk group, the number of BMD tests performed for each subgroup, and the outcomes of the BMD measurement (normal, osteopaenia or osteoporosis and number of abmormal BMD over all BMD measurements conducted per subgroup). Continuous variables were reported as medians and ranges and compared using Kruskal-Wallis test. Categorical variables were reported as frequencies and percentages and compared using Chi-squared test. Statistical tests were performed using R-4.3.1. A run chart of BMD measurements was also generated using dates of BMD conduction. Missing dates were imputed using frequent category imputation. Although the protocol made no recommendations for BMD among male patients using OSTA scoring, an exploratory analysis was conducted on the BMD outcomes of male patients who underwent BMD testing (whether based on their FRAX score or shared decision-making with their clinicians). This was based on studies suggesting that OSTA may also be used to predict osteoporosis risk in male individuals, including Asian men.^15–17

Results

During the study period, 6,619 adult patients with chronic diseases were seen in the various participating primary care clinics. Of these patients, 287 patients had osteoporosis at baseline and were excluded. Of the 6,332 remaining patients, 5,420 (81.1%) underwent OSTA scoring whereas 972 (14.6%) did not have weight measurements (Table 1).

Table 1.

Characteristics of included patients.

Characteristics	All patients with OSTA score (n = 5,420)
Age (median, years)	64 (range 20-102)
Sex (n, %)
Male	2,904 (53.6)
Female	2,516 (46.4)
Ethnicity (n, %)
Chinese	4,441 (81.9)
Malay	619 (11.4)
Indian	267 (4.9)
Eurasian	3 (<0.1)
Others	90 (1.6)
Weight (median, kg)	66 (range 26.6-178)
Height (median, cm)	161 (range 121-193)
BMI (median, kg/m²)	25.2 (range 12.2-62.9)
Comorbidities (n, %)
Diabetes	1,922 (35.5)
Hypertension	4,023 (74.2)
Dyslipidaemia	4,474 (82.5)
Asthma	325 (6.0)

The majority of the patients (3,517; 64.9%) were OSTA low risk whereas 1,521 patients (28.1%) were intermediate risk and 421 (7.0%) were high risk (Table 2 and Figure 2). Patients in the high-risk group were significantly older and had a higher proportion of female patients (60.7%) compared with other risk groups. Furthermore, the high-risk group had lower median weight, height and BMI. They also had a higher proportion with a baseline history of hypertension (84.0%). On the other hand, the low-risk group had a lower proportion with baseline diabetes (33.5%) and dyslipidaemia (79.4%).

Table 2.

Characteristics of patients by OSTA risk category.

Characteristics	Low risk (score <0) (n = 3,517)	Intermediate risk (score 0-20) (n = 1,521)	High risk (score >20) (n = 382)	p-value
Age (median, years)	58 (range 20-94)	71 (range 35-94)	79 (range 60-102)	<0.0001^a
Sex (n, %)				<0.0001^b
Male	2,057 (58.5)	697 (45.8)	150 (39.3)
Female	1,460 (41.5)	824 (54.2)	232 (60.7)
Ethnicity (n, %)				<0.0001^b
Chinese	2,798 (77.6)	1,295 (85.1)	348 (91.1)
Malay	440 (12.5)	156 (10.3)	23 (6.0)
Indian	202 (5.7)	56 (3.7)	9 (2.4)
Eurasian	3 (<0.1)	0	0
Others	74 (2.1)	14 (0.9)	2 (0.5)
Weight (median, kg)	72.0 (range 39-178)	57.2 (range 26.6-81.6)	47.4 (range 28.7-66.5)	<0.0001^a
Height (median, cm)	164 (range 121-193)	157 (range 122-183)	151 (range 124-181)	<0.0001^a
BMI (median, kg/m²)	26.7 (range 16.0-62.9)	23.1 (range 12.2-41.8)	20.7 (range 12.6-31.2)	<0.0001^a
Comorbidities (n, %)
Diabetes	1,178 (33.5)	568 (37.3)	132 (34.6)	0.0310^b
Hypertension	2,518 (71.6)	1,170 (76.9)	321 (84.0)	<0.0001^b
Dyslipidaemia	2,794 (79.4)	1,345 (88.4)	335 (87.7)	<0.0001^b
Asthma	218 (6.1)	85 (5.6)	22 (5.8)	0.6900^b

^aKruskal-Wallis test.

^bChi-squared test.

Figure 2.

Flowchart of patients based on OSTA risk score. Outlined box indicates protocol criteria (i.e., Females with high risk or females with moderate OCTA score and DM). BMD, bone mass density; DM, diabetes mellitus, OSTA, osteoporosis self-assessment tool for Asians.

Overall, 206 patients underwent BMD testing in the primary care clinics during the programme period; however, 41 of these patients had baseline osteoporosis and were excluded. Of the patients with an OSTA score 165 underwent BMD measurement. Only 38 of the 531 eligible patients (7.1%) based on the protocol criteria (i.e., females with high risk or females with moderate OSTA score and DM) (Figure 2). There were also 127 additional patients with OSTA scores outside the protocol criteriawho had BMD measurement based on shared decision-making between the patient and their physician. Only 11 patients stated their reason for refusal to undergo BMD testing: five patients mentioned they were not bothered by the high OSTA score; three patients found the test inconvenient; and the remaining three gave other unspecified reasons.

Figure 2 also summarises the outcomes of the BMD tests. Among the 38 patients with BMD measurement based on the protocol criteria, 11 patients (28.9%) had abnormal BMD results (4 patients had osteopaenia and seven patients had osteoporosis). The detection rate for abnormal BMD was highest among male and female patients with high OSTA score (41/3 [33.3%] and 4/15 [26.7%], respectively), and among women with DM and intermediate risk score (7/23 [30.4%]. Notably, among females without DM with intermediate risk, the rate was 20.4% (10/49). Among males with intermediate risk, detection rate was 13.3% (2/15). In the low-risk group, the detection rate for abnormal BMD was 47.8% (1/23) among males and 13.5% (5/37) among females.

Only 939 patients (17.3%) consented to the performance of FRAX scoring. Figure 3 summarizes the patient flow for FRAX scoring. Almost a third of patients (296, 31.5%) had a ≥3% 10-year probability of a hip fracture (high hip fracture risk) based on FRAX (no BMD), including 41 patients that had a ≥20% 10-year probability of a major fracture (high major fracture risk) by FRAX (no BMD). Only eight patients with high FRAX risk score (without BMD) eventually underwent BMD testing; one patient (12.5%) had osteoporosis on BMD. Of the 14 patients with FRAX score with BMD, eight had a high FRAX risk score and one patient (12.5%) was found to have osteoporosis.

Figure 3.

Flowchart of patients with FRAX scoring. BMD, bone mass density; FRAX, fracture risk assessment tool.

Finally, Figure 4 shows the impact of the programme on the time-trend for BMD measurements. The run chart indicates a marked increase in the uptake for BMD measurement during the programme implementation.

Figure 4.

Run chart of BMD measurements before and after the programme implementation. BMD, bone mass density; COVID-19, coronavirus disease 2019.

Discussion

The outcomes of the programme showed that a “Predict and Prevent” workflow could be implemented to screen, detect and potentially treat patients at high risk of osteoporosis. Specifically, we were able to show that 85.3% of patients with chronic conditions seen at primary care clinics were assessed for the risk of osteoporosis using OSTA scoring. We were also able to show that only 16.8% of those screened using OSTA proceeded to further fracture risk assessment via FRAX. Only 180 patients with an OSTA score, including only 40 of the 589 eligible patients (6.8%) underwent BMD measurement.

This programme was started on January 2020 and ran until December 2021. However, coronavirus disease 2019 (COVID-19)-related restrictions were implemented starting 03 April 2020 and remained in effect in various degrees until April 2022.^18,19 The reduced mobility from these restrictions, as well as the fear of many patients to leave their homes, may have substantially affected the implementation of the programme. For instance, the fear of stepping into the clinics reduces the opportunity for clinic-based weight measurement and may have contributed to the non-negligible number of patients who could not be assessed via OSTA scoring. Furthermore, these COVID-19 restrictions could partly explain the low number of BMD testing done throughout the programme. Of the limited number of patients that provided reasons against BMD measurement, the two most common were being unbothered by their OSTA score and the inconvenience of testing. These qualitative reasons warrant further exploration to help improve programme implementation. Nonetheless, they highlight the low priority patients may attribute to silent conditions such as osteoporosis. These may be addressed, in part, through patient education. Among women aged at least 65 years seen at two polyclinics in Singapore who underwent a survey combined with osteoporosis education, 63.6% of participants were willing to undergo screening.²⁰

Despite the limitations faced during the COVID-19 pandemic, the programme was able to demonstrate that screening tools such as the OSTA could be easily implemented in the primary care setting. Given the substantially fewer parameters required for OSTA scoring, this tool could be used on almost all adult patients. Data from Well-being of the Singapore Elderly (WiSE), a comprehensive single-phase, cross-sectional population-based epidemiological survey on Singapore residents aged 60 years and above found that 52% had an OSTA score of less than −1 compared with 36.3% reported in our programme.²¹ WiSE also found that high osteoporosis risk was associated with older age, female sex and Chinese ethnicity, which aligns with the experience in our programme.

The implementation of FRAX proved more challenging, with only 16.8% of patients with OSTA consenting to FRAX scoring. The complexity of FRAX compared with OSTA could partly explain its poorer implementation is it requires more variables, some of which may require accurate patient knowledge or recall (e.g., hip fracture in a parent or secondary cause of osteoporosis, such as hyperparathyroidism, multiple myeloma, Cushing’s disease, coeliac disease or hyperthyroidism).²² Other challenges associated with FRAX include the limited validation in Asians, uncertainty about the range of error with fracture risk, and the lack of validation with non-dual x-ray absorptiometry BMD technologies.²³ Because of the challenges associated with FRAX, previous research has compared the outcomes of OSTA and FRAX as screening tools. A community-based study on 2055 perimenopausal Han Chinese women found that the area-under-the-curve (AUC) values for FRAX without BMD (hip fracture risk) was 0.796, which was similar to the AUC of OSTA (0.798).²² The sensitivities (74.79% and 69.64%, respectively) and specificities (70.45% and 75.07%, respectively) of the two tests were also similar. Importantly, the study found that the use of FRAX and OSTA allowed 42.4% and 37.6% of participants to avoid BMD testing while only missing 7.2% and 8.6% of individuals with osteoporosis. A local study involving 1056 Singaporean postmenopausal women also found that OSTA and FRAX performed similarly in identifying osteoporosis. The authors of this study also concluded that while OSTA may be simpler to use, FRAX may be used in primary screening to identify postmenopausal woman that needs to be referred for BMD measurement and may help facilitate discussions with patients regarding fracture risk.²⁴

Some studies have shown that the use of FRAX without BMD may result in the overestimation of fracture risk. A Brazilian cross-sectional study on women older than 40 years with BMD done in the previous year of the study period found that FRAX scores were higher when BMD measurement was not included (Spearman correlation coefficients of r = 0.793 (95% CI 0.7388‒0.836) for major fracture and r = 0.6922 (95% CI 0.6174‒0.75446) for hip fractures.²⁵ A smaller study found that fracture risk was overestimated by FRAX without BMD in patients older than 65 years (p < .0001).²⁶ Other studies have shown good concordance between FRAX with and without BMD.²⁷ A Thai cross-sectional study conducted in patients between 40 and 90 years of age found that while the majority (83.8%) showed concordance between FRAX with and without BMD, concordance decreased in the elderly, those with osteoporosis, or those with FRAX without BMD around the intervention threshold.²⁸

Some authorities such as the Osteoporosis Canada Fracture Liaison Service COVID-19 task force have recommended the use of FRAX without BMD to guide treatment when BMD cannot be performed and osteoporosis treatment initiated in those who scored at high risk using FRAX without BMD.²⁹ However, the task force also recommended that BMD testing should be completed when available.

Given the non-universal accessibility of BMD measurement and the similarity of the diagnostic utility of OSTA and FRAX, OSTA may be used as an initial screening tool in the primary care setting. Taking cue from Chandran et al (2020),²⁴ FRAX scoring to estimate fracture risk may then be considered in the clinical decision-making and treatment cascade for osteoporosis by identifying patients that should be prioritised for BMD testing. The administration of the FRAX scoring by nurses also provides a good opportunity to impart osteoporosis education. Clinicians should be aware that FRAX without BMD may overestimate fracture risk in certain subsets, such as the elderly. The real-world diagnostic yield of this strategy should also be investigated in formal research.

One way to potentially increase the update of FRAX scoring is through remote screening. Among the 4,481 patients without FRAX scoring, only 788 patients (17.6%) consulted remotely. In contrast, remote consultation was undertaken by 97.2% (913/939) of patients with FRAX scoring. This simple intervention allows patients to undergo risk scoring without substantial investment in time and effort for clinic visits.

Our experience also suggests that a history of other chronic comorbidities, such as hypertension and dyslipidaemia, and DM to a lesser extent, is associated with OSTA-assessed osteoporosis risk. The association of osteoporosis with hypertension and dyslipidaemia has been previously demonstrated in other studies.^6,30–32 In contrast, a meta-analysis has confirmed that there is no relationship between type 2 DM and low BMD.³³ Our results even suggested that female patients without DM and intermediate-risk OSTA score have higher detection rates versus those with DM. While some studies in Singapore have mentioned a possible protective effect of DM,²¹ we do not attempt to make a similar assertion as our findings should be interpreted with caution. We acknowledge that chronic conditions such as hypertension, dyslipidaemia and DM are influenced by several confounders, including age and weight, which are key determinants of the OSTA risk score. Instead, we suggest that female patients with intermediate OSTA score should be offered BMD measurement regardless of DM status. The associations we also found between osteoporosis and other chronic diseases suggest that osteoporosis screening should be included as part of the holistic care of patients with these conditions.

Several limitations need to be considered. Firstly, this prevention programme was not designed as a research study. Therefore, no blinding or randomization was performed, which could introduce bias. Additionally, only a limited amount of information required for programme evaluation and performance was made available to the authors due to the nature of this paper. This also limited the types and extent of data analysis that could be performed on the limited available data. The sparsity of qualitative data systematically collected also limited the ability of the authors to evaluate the programme using systematic implementation research frameworks, such as evaluation frameworks (e.g., RE-AIM).³⁴ The exploratory and post-hoc nature of the data analysis is also an important limitation to note. The counselling method with regard to the need for BMD testing and pharmacotherapy, which may impact the willingness of patients to undergo further interventions, was also not standardised. Lastly, the programme was implemented at a single network of private primary care clinics, which would limit the generalisability of the reported findings.

Conclusion

Our experience showed that a “Predict and Prevent” workflow could be implemented to screen, detect and potentially treat patients at high risk of osteoporosis. Using OSTA as a screening tool, almost four out of 10 patients with chronic disease seen in a network of primary care clinics in Singapore during the COVID-19 pandemic were at risk of osteoporosis. Only less than a fifth of patients underwent further fracture risk assessment using FRAX scoring. OSTA may be used as an initial screening tool in the primary care setting and FRAX scoring to estimate fracture risk may then be considered to guide clinical decision-making. The rate of BMD measurement is low and needs to be improved.

Availability of data: Limited patient-level data is available upon request to the corresponding author.

Footnotes

Acknowledgements

The authors acknowledge Dr Ivan Olegario for providing medical writing support.

Author contributions

All authors have accepted responsibility for the entire content of this manuscript and approved its submission. All authors contributed to the programme design, programme implementation, and manuscript preparation, editing and approval.

Declaration of conflicting interests

The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors are employees of Frontier Healthcare.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work and the screening programme were supported by Amgen.

ORCID iDs

Chin Kwang Chong

Tat Yean Tham

References

Mithal

Dhingra

Lau

. The Asian audit: Epidemiology, costs and burden of osteoporosis in Asia. Beijing, China: An International Osteoporosis Foundation (IOF) publication, 2009.

Chandran

Lau

Gagnon-Arpin

, et al. The health and economic burden of osteoporotic fractures in Singapore and the potential impact of increasing treatment rates through more pharmacological options. Arch Osteoporos 2019; 14: 114.

Chandran

Mitchell

. Tackling osteoporosis and fragility fractures in Singapore. Ann Acad Med Singap 2021; 50: 291–293.

Wee

Min

Park

, et al. The association of asthma and its subgroups with osteoporosis: a cross-sectional study using KoGES HEXA data. Allergy Asthma Clin Immunol 2020; 16: 84.

McFarlane

Muniyappa

Shin

, et al.

Osteoporosis and cardiovascular disease: brittle bones and boned arteries, is there a link?

Endocrine 2004; 23(1): 1–10.

Zhang

Liu

Zeng

, et al. Association of dyslipidaemia with osteoporosis in postmenopausal women. J Int Med Res 2021; 49(3): 300060521999555.

Chai

, et al. Hypertension is associated with osteoporosis: a case-control study in Chinese postmenopausal women. BMC Musculoskelet Disord 2021; 22(1): 253.

Ross

Lee

Harris

, et al. The impact of diabetes on osteoporosis management and secondary fracture risk after primary fragility fractures: a propensity score-matched Cohort study. J Am Acad Orthop Surg 2022; 30(2): e204–e212.

Schacter

Leslie

. Diabetes and osteoporosis: Part I, Epidemiology and pathophysiology. Endocrinol Metab Clin North Am 2021; 50(2): 275–285.

10.

Agency for Care Effectiveness . Appropriate care guide – osteoporosis: identification and management in primary care. Republic of Singapore: Ministry of Health, 2018, www.ace-hta.gov.sg (Accessed on 20 June 2023).

11.

Koh

Sedrine

Torralba

, et al. A simple tool to identify asian women at increased risk of osteoporosis. Osteoporos Int 2001; 12(8): 699–705.

12.

Kanis JA on behalf of the World Health Organization Scientific Group . (2007). Assessment of osteoporosis at the primary health-care level. Technical Report. World health Organization collaborating Centre for metabolic bone diseases. Sheffield, UK: University of Sheffield, 2007.

13.

University of Sheffield

UK.

FRAX fracture risk assessment tool. Sheffield, UK: University of Sheffield, 2023. Available at: https://frax.shef.ac.uk/FRAX/tool.aspx?country=1 (Accessed on 20 June 2023).

14.

Kanis

Harvey

Johansson

, et al. FRAX update. J Clin Densitom 2017; 20: 360–367.

15.

Kung

Ross

, et al. Development of a clinical assessment tool in identifying Asian men with low bone mineral density and comparison of its usefulness to quantitative bone ultrasound. Osteoporos Int 2005; 16(7): 849–855.

16.

Gadong

LCP

Cabral

Capellan

, et al. Prognostic performance of predictive Index for osteoporosis and osteoporosis self-assessment tool for Asians in the identification of individuals high-risk for osteoporosis. Osteoporos Sarcopenia 2020; 6(3): 115–121.

17.

Mendoza

Lopez

Valdez

, et al. Osteoporosis and prevalent fractures among adult Filipino men screened for bone mineral density in a tertiary hospital. Endocrinol Metab (Seoul) 2016; 31(3): 433–438.

18.

Prime Minister’s Office . PM lee Hsien Loong on the COVID-19 situation in Singapore on 3 April 2020. 2020. Available at: https://www.pmo.gov.sg/Newsroom/PM-Lee-Hsien-Loong-on-the-COVID19-situation-in-Singapore-on-3-April-2020 (Accessed on 20 June 2023).

19.

Ministry of Health . Further easing of community and border measures. 2023. Available at: https://www.moh.gov.sg/news-highlights/details/further-easing-of-community-and-border-measures (Accessed on 20 June 2023).

20.

Lulla

Teo

Shen

, et al. Assessing the knowledge, attitude and practice of osteoporosis among Singaporean women aged 65 years and above at two Sing health polyclinics. Singapore Med J 2021; 62(4): 190–194. DOI: 10.11622/smedj.2021039.

21.

Wang

Abdin

Shafie

, et al. Estimation of prevalence of osteoporosis using OSTA and its correlation with sociodemographic factors, disability and comorbidities. Int J Environ Res Public Health 2019; 16(13): 2338. DOI: 10.3390/ijerph16132338.

22.

Fan

Zhang

, et al. Comparison of OSTA, FRAX and BMI for predicting postmenopausal osteoporosis in a han population in Beijing: A cross sectional study. Clin Interv Aging 2020; 15: 1171–1180. DOI: 10.2147/CIA.S257166.

23.

Leib

Saag

Adachi

, et al. Official positions for FRAX® clinical regarding glucocorticoids: the impact of the use of glucocorticoids on the estimate by FRAX® of the 10 year risk of fracture from joint official positions development conference of the International society for clinical densitometry and International osteoporosis foundation on FRAX®. J Clin Densitom 2011; 14: 212–219. DOI: 10.1016/j.jocd.2011.05.014.

24.

Chandran

Chin

Choo

, et al. Comparison of the osteoporosis self-assessment tool for Asians and the fracture risk assessment tool - FRAX to identify densitometric defined osteoporosis: a discriminatory value analysis in a multi-ethnic female population in Southeast Asia. Osteoporos Sarcopenia 2020; 6(2): 53–58.

25.

Favarato

MHS

Almeida

Lichtenstein

, et al. Risk of osteoporotic fracture in women using the FRAX tool with and without bone mineral density score in patients followed at a tertiary outpatient clinic ‒ an observational study. Clinics 2022; 77: 100015. DOI: 10.1016/j.clinsp.2022.100015.

26.

Kaur

Mendez

Vig

, et al. FRI0492 Calculating FRAX score in clinical practice: pitfalls and problems. Annals Rheumatic Diseases 2019; 78(Suppl 2): 941.1.

27.

Gadam

Schlauch

Izuora

. FRAX prediction without BMD for assessment of osteoporotic fracture risk. Endocr Pract 2013; 19(5): 780–784. DOI: 10.4158/EP12416.OR.

28.

Teeratakulpisarn

Charoensri

Theerakulpisut

, et al. FRAX score with and without bone mineral density: a comparison and factors affecting the discordance in osteoporosis treatment in Thais. Arch Osteoporos 2021; 16(1): 44. DOI: 10.1007/s11657-021-00911-y.

29.

Osteoporosis Canada . Using FRAX without BMD during the Covid pandemic. 2023. Available at: https://fls.osteoporosis.ca/wp-content/uploads/Use-of-FRAX-without-BMD-during-pandemic.pdf (Accessed on: 20 June 2023).

30.

Vestergaard

Rejnmark

Mosekilde

. Hypertension is a risk factor for fractures. Calcif Tissue Int 2009; 84: 103–111.

31.

Caudarella

Vescini

Rizzoli

, et al. Salt intake, hypertension, and osteoporosis. J Endocrinol Invest 2009; 32(Suppl. 4): 15–20.

32.

Anagnostis

Florentin

Livadas

, et al. Bone health in patients with dyslipidemias: an underestimated aspect. Int J Mol Sci 2022; 23(3): 1639. DOI: 10.3390/ijms23031639.

33.

Qiu

Dong

, et al. Is diabetes mellitus a risk factor for low bone density: a systematic review and meta-analysis. BMC Endocr Disord 2021; 21(1): 65. DOI: 10.1186/s12902-021-00728-3.

34.

Glasgow

Vogt

Boles

. Evaluating the public health impact of health promotion interventions: the RE-AIM framework. Am J Public Health 1999; 89: 1322–1327.

Performance of a preventive care programme for osteoporosis in primary care settings in Singapore during the COVID-19 pandemic

Abstract

Background

Objective

Methods

Results

Conclusion

Keywords

Introduction

Methods

Included patients

Programme design

Data extraction and analysis

Results

Discussion

Conclusion

Footnotes

Acknowledgements

Author contributions

Declaration of conflicting interests

Funding

ORCID iDs

References