Modifiable prognostic factors of high costs related to healthcare utilization among older people seeking primary care due to back pain: an identification and replication study

The burden of back pain has been growing along with an increasing and ageing population [1‐5]. Back pain is the number one cause of disability globally [4] and an extensive burden to our healthcare systems [2, 6‐8]. According to a recent systematic review, the prevalence rate of healthcare utilization for back pain ranges from 28 to 92% [9]. Back pain is one of the most prevalent complaints encountered in primary care [4, 5, 8, 10].

To improve use of scarce healthcare resources and reduce the burden on our healthcare systems, where possible and appropriate, researchers have highlighted the importance of monitoring and understanding healthcare utilization and related costs [3, 11]. It is well known that most of healthcare utilization and related costs stems from a relatively small group of back pain patients [12], and more importantly, that many of these patients receive unnecessary and ineffective treatment [3, 7]. This suggests that care for this high-cost subgroup requires quality improvement and cost reduction. An important next step towards this would be to identify modifiable prognostic factors associated with high costs related to healthcare utilization, and to replicate initial findings to evaluate the consistency of prognostic value across datasets and settings [13]. Information about such factors could inform development of effective strategies and/or interventions, or new applications of existing interventions.

Only a few prospective studies have explored modifiable prognostic factors associated with high costs related to healthcare utilization among patients with back pain [14‐17], and no such study has been conducted among a sample of exclusively older people with back pain. Patients with high costs related to healthcare utilization are a diverse population [18, 19], and generalization of results cannot be done automatically from younger to older people with back pain [20]. With an ageing population, and the expected rise in older people requiring back care in the years to come [21], it is important to study modifiable prognostic factors of high costs related to healthcare utilization among older people with back pain.

Therefore, the aims of this study were 1) to identify modifiable prognostic factors for high costs related to healthcare utilization among older people seeking primary care with a new episode of back pain and 2) to replicate the identified associations in a similar cohort, in a different country.

This study was designed and performed in accordance with the PROGnosis RESearch Strategy (PROGRESS) framework [22], with aims consistent with prognostic factor research: identification of prognostic factors, including external replication. In line with recommendations from the PROGRESS framework [13] a study protocol (ClinicalTrials.​gov NCT04261309, 07 February 2020) including a statistical analysis plan has been published [23], and the REMARK reporting guidelines were followed [24].

A total of 452 (BACE-N) and 675 (BACE-D) patients were included in the identification and the replication sample, respectively. Table 2 shows patient characteristics and clinical status at baseline, along with the proportion of missing data per variable. Flow of patients through the studies are shown in Fig. 1. Fourteen patients (3%) in BACE-N and 22 patients (3%) in BACE-D were dropouts at 12-months follow-up. We removed these cases from the analyses. Within BACE-N, there was a larger proportion of females (55 vs. 42%) among the responders as compared to non-responders. Within BACE-D, there was a larger proportion of people not in paid work (26 vs. 38%) and people with short pain duration < 6 weeks (56 vs. 39%) among the responders as compared to non-responders. Otherwise, there were no differences between responders and non-responders in the two cohorts. The BACE-N and BACE-D samples were also largely comparable, although there were some differences that might have impacted healthcare utilization. BACE-N had a larger proportion of people with high education level (44 vs. 17%), people in paid work (47 vs. 27%), and people with short pain duration < 6 weeks (67 vs. 54%).

Within BACE-N, missing data ranged from 0.0 to 16.8% for included baseline variables and 18.4 to 26.0% for included follow-up variables. Total missingness was 4.9 and 23.3% for all baseline and follow-up values, respectively. Variables on medication use at 12-months follow-up had most missing values. Within BACE-D, missing data ranged from 0.0 to 11.8% for included baseline variables and 7.3 to 18.1% for included follow-up variables. Total missingness was 2.2 and 9.6% for all baseline and follow-up values, respectively. Variables on examination at 12-months follow-up had most missing values.

The present study identified and replicated associations between modifiable prognostic factors and high costs related to healthcare utilization among older people seeking primary care with a new episode of back pain. Four modifiable prognostic factors associated with high costs related to healthcare utilization were identified and replicated in a similar cohort, in a different country, reflecting slightly different sociodemographic characteristics and healthcare setting: pain severity, disability, depression and physical health-related quality of life. Kinesiophobia and expectations of recovery showed no prognostic value. There were inconsistent results across the two cohorts with regards to comorbidity, radiating pain below the knee and mental health-related quality of life.

To the best of our knowledge, no similar study has been conducted among a sample of exclusively older people with back pain. Thus, direct comparability of this study with other studies is limited. Nevertheless, our findings are generally in accordance with previous research on (primarily) middle-aged back pain patients [14‐16, 28, 29, 32, 35], as well as patients with musculoskeletal disorders [30, 54, 55]. For example, pain severity, disability and depression have been shown to be significantly associated with high costs related to healthcare utilization in studies on patients with back pain [14‐16, 28, 29, 32, 35] and musculoskeletal disorders [30, 54, 55]. Physical health-related quality of life has also previously been reported to be a prognostic factor of high societal costs among back pain patients [14], and high costs related to healthcare utilization among patients with musculoskeletal disorders [30]. Our findings regarding kinesiophobia and radiating pain below the knee are also in line with a previous study [15], which showed that these factors were of minor importance when predicting future costs related to healthcare utilization among back pain patients. Our finding regarding mental health-related quality of life is, however, contrary to a study on patients with musculoskeletal disorders [30], which found that this factor was associated with persistent high costs related to healthcare utilization. Our finding regarding comorbidity is also contrary to previous research. In a recent systematic review, comorbidity was pointed out as a consistent prognostic factor of high costs related to healthcare utilization in general [18], and similar conclusions have been drawn in single studies among patients with back pain [17] and musculoskeletal disorders [55]. This discrepancy might be explained by the fact that we included costs related to back pain specific healthcare utilization, whereas other studies have included healthcare costs related to all musculoskeletal disorders [17, 55] and healthcare costs in general [18]. To the best of our knowledge, the prognostic value of recovery expectations for high costs related to healthcare utilization has not been reported previously.

The main limitation of this study is missing data on variables used to estimate the outcome score, thus we had to manually replace missing values. It is well-known that healthcare utilization is prone to missing data [56‐58]. Also, that missing data should be replaced in order to make use of all reported data [56, 57]. Unfortunately, due to poor predictive performance, multiple imputation could not be used on follow-up data in this study. We therefore chose a frequently used, though not optimal, method for replacing missing values [58] and have tried to be transparent in our reporting. A second potential limitation is that we used self-reported data on healthcare utilization. Self-reports tend to underestimate the true value of healthcare utilization due to potential recall bias [59‐62]. Nevertheless, we consider the impact of recall bias to be of only minor importance in this study as the outcome variable was dichotomized into high or low costs. In future studies, the limitations of missing data and recall bias could to some extent be overcome by including registry data on healthcare utilization. A third potential limitation is that costs related to hospitalization and rehabilitation stays were not measured in BACE-D. Thus, the risk of misclassification bias related to whether patients were classified as having high or low costs might be present in BACE-D. However, if costs related to hospitalization and rehabilitation stays were removed from the cost calculations in BACE-N, only 4 patients (< 1%) switched cost group. A fourth potential limitation is that we could not adjust for possibly important covariates of healthcare utilization, such as the patient’s disposition to access and pay for healthcare services, and health insurance status. According to the Behavioral Model of Health Services Use from Andersen [63], healthcare utilization is a function of people’s predisposition to use services, factors which enable or impede use and need for care. Certainly, including these enabling factors is recommended. However, it is likely to assume that these factors are of less importance in countries such as Norway and the Netherlands, where health services are largely available and covered by the public sector. A fifth limitation is the lack of data on eligible participants that declined to participate or for other reasons were not invited. Due to limited resources and practical reasons related to recruitment from a broad network of clinicians, it was not possible to record information on all eligible participants during the BACE-N and BACE-D data collection period. Thus, the risk of selection bias is present. To compensate for this limitation and assess representativeness of the BACE-N sample, key sociodemographic variables have been compared with a large population study on older people; The Norwegian study on life course, ageing and generation (NORLAG) [64, 65]. A subsample of the NORLAG (NORLAG MSK) was used, which is expected to be a representative sample of people aged ≥55 years with musculoskeletal complaints. Characteristics of the two samples were largely comparable, though BACE-N had more men, and more with higher education level [65]. Previous studies have shown that women [14, 28, 29] are more likely to seek care for their back pain as are people with lower education level [28, 32, 41]. Hence, it is likely to assume that the amount of healthcare utilization presented in BACE-N is somewhat underestimated. Furthermore, the BACE-N sample is largely comparable to younger Norwegian back pain cohorts [66, 67] and to the BACE-D sample [26].

The main strength of the present study is that it was conducted in line with the PROGRESS framework including an identification and replication phase [13], pre-planned with a published statistical analysis plan, and reported in line with the REMARK guidelines [24]. Also, that it estimates the prognostic value of modifiable prognostic factors over and beyond a core set of non-modifiable covariates. Prognostic factor studies are an essential step towards quality improvement of clinical practice [13]. Results from such studies have the potential to inform development of effective strategies and/or interventions. Identifying modifiable prognostic factors of high costs related to healthcare utilization among older people is an important step towards addressing the global burden of back pain and decrease waste of valuable healthcare resources [3, 7, 68].

In conclusion, this study identified and replicated four modifiable prognostic factors associated with high costs related to healthcare utilization among older people seeking primary care with a new episode of back pain: pain severity, disability, depression, and physical health-related quality of life. This study contributes to the on-going research into clinical pathways and has the potential to identify future target areas for intervention with the potential to reduce high costs related to healthcare utilization among older back pain patients. Due to differences in healthcare systems between countries, readers are advised to exercise caution with generalizability of the results to other healthcare systems.