Abstract
Background
In Japan, provision of equal access to cancer care is intended to be achieved via secondary medical areas (SMAs). However, the percentage of patients receiving care within the residential area varies by SMA in Osaka Prefecture. We aimed to assess the effect size of factors associated with patient mobility, and whether patient mobility was affected by the COVID-19 pandemic.
Methods
Records of patients diagnosed with stomach, colorectal, lung, breast, cervical, oesophageal, liver or pancreatic cancer during 2019–2020 were extracted from multi-centre hospital-based cancer registry data. Odds ratios of whether a patient received care within the SMA of residence were set as the outcome. A multivariable model was built using generalised estimating equations with multiple imputation for missing data. Change in patient mobility after the pandemic was examined by deriving age- and SMA-specific adjusted ORs (aORs).
Results
A total of 78,839 records were included. Older age, more advanced stage and palliative care had up to 1.69 times higher aORs of receiving care within their own area. Patients with oesophageal, liver or pancreatic cancer tended to travel outside their area with aORs ranging from 0.71 to 0.90. Patients aged ≤ 79 and living in the East and South SMAs tended to remain in their area with aORs ranging from 1.05 to 1.11 after the pandemic.
Conclusion
Patient mobility decreased for higher age and stage. It also varied by SMA, cancer site and treatment type.
Policy Summary
Our results need to be linked with resource inputs to help policymakers decide whether to intervene to address current efficiency or equity issues.
Keywords: Cancer care, CanReCO, COVID-19, Japan, Patient mobility
1. Introduction
Providing equitable and efficient care is the ultimate goal for healthcare in many countries. In Japan, since the foundation of the Basic Plan to Promote Cancer Control Programs (Basic Plan) in 2007 [1], the policy priority for cancer care has been to facilitate equitable access. The goal was to ensure appropriate, acceptable and standardised quality care for all by encouraging care coordination [2]. Secondary medical areas (SMAs) have been established to cover emergency and general inpatient care within their catchment area. For cancer care, designated cancer care hospitals (DCCHs) have been set up for each SMA nationwide following the Basic Plan [3], [4]. Despite this aim, however, there are still disparities in access and survival according to patients’ geographical location or socioeconomic status [5], [6]. In Osaka Prefecture, patient mobility, assessed by the proportion of people receiving cancer care within their residential area (RA), varies widely across SMAs. This situation has caused a long-standing issue of technical inefficiency with the waste of resource inputs [7], [8]. It also raises questions about access inequalities because patients might have travelled outside their own RA to seek more timely, higher-quality care.
Besides these persistent inequalities, the coronavirus disease 2019 (COVID-19) pandemic could have exacerbated difficulties in access to cancer care for some populations. When the first surge of the COVID-19 pandemic began, a state of emergency (SoE) was declared in early April 2020 and lasted for nearly two months in Japan [9]. The SoE rules were not as restrictive as the lockdowns in other countries; the Government advised people to avoid unnecessary travel crossing prefectural borders but with no penalty for non-compliance. A reduced number of diagnoses and screen-detected cases have been reported [10], [11], implying that some people might have altered their travelling patterns in accessing cancer care. Amid the pandemic, factors associated with access to cancer care have not yet been fully evaluated. Furthermore, the pandemic might have widened the access gap; however, the affected population has not been identified.
A Cancer Registry-based Study on the Impact of COVID-19 on Cancer Care in Osaka (CanReCO) began in 2021 to assess how COVID-19 affected cancer care and outcomes [11]. As a part of the CanReCO project, in this study we aimed to 1) evaluate the effect of potentially associated factors on receiving care within the SMA of residence in Osaka Prefecture and 2) identify which populations were particularly affected by COVID-19 by comparing the periods before and after the pandemic defined by the declaration of SoE (DSE). We focused on the five most common cancers, which have screening programmes in Japan (stomach, colorectal, lung, breast and cervical cancer) and the three most common gastrointestinal cancers (oesophageal, liver and pancreatic cancer).
2. Methods
2.1. Study settings
Osaka Prefecture is the third most populous prefecture (8.8 million) in Japan, and has eight SMAs. The SMAs in the prefecture comprise Osaka City (the City) located in the centre of the crescent-shaped prefecture ( Fig. 1), Toyono (north of the City), Mishima (north-northeast), Kita-Kawachi (northeast), Naka-Kawachi (east), Minami-Kawachi (southeast), Sakai (south) and Senshu (southwest), characterised by a relatively younger population in the City and North [12]. There are a number of requirements for accreditation as a DCCH and these are more onerous for national than prefectural DCCHs in terms of facilities, the number of specialists and the patient volume achieved (e.g., annual surgical volume ≥400 for national and ≥200 for prefectural DCCHs) [3]. Over 80% of the patients diagnosed in the prefecture undergo surgical treatment in DCCHs [13], [14]. Japan has no strong gate-keeping system regarding patient access to medical facilities, and people have freedom of choice as to where they receive care. Although both public and private care providers exist, medical costs under the social insurance system are identical across the nation due to the universal fee schedule [15]. People can access any DCCH across Japan with or without a referral, but the common choice is to obtain a referral. Without a referral, patients pay an additional fee of at least US$ 50 (2020) [15].
Fig. 1.
Demographic characteristics of secondary medical areas in Osaka Prefecture, Japan. Abbreviations: DCCHs, designated cancer care hospitals. Upper case letters in brackets after the area names represent directions from Osaka City.
Source: 1. National Land Information Division, National Spatial Planning and Regional Policy Bureau, MLIT of Japan. Medical area data. Tokyo, 2014. Available from: https://nlftp.mlit.go.jp/ksj/gml/datalist/KsjTmplt-A38.html. (Accessed 02 May 2022). 2. Japan Medical Association. JMAP (Japan Medical Analysis Platform), Osaka Prefecture. Tokyo: 2021. Available from: https://jmap.jp/cities/detail/pref/27. (Accessed 10 Mar 2022). 3. Cancer Control Center, Osaka International Cancer Institute. [Designated cancer care hospitals in Osaka Prefecture (in Japanese)]. Osaka: 2022. Available from: https://osaka-gan-joho.net/cnow/map/. (Accessed 27 May 2022).
2.2. Data sources
The CanReCO project was a multi-centre cohort study in Osaka Prefecture [11]. Records of patients diagnosed with primary cancer between 1 January 2019 and 31 December 2020 were collected as hospital-based cancer registry data after anonymisation at 66 out of the 67 DCCHs in the Prefecture.
In the registry data, details on patient factors, including age at diagnosis, sex, date of birth and SMA of residence are recorded. Other information on tumour and treatment factors included: site of cancer defined by International Classification of Diseases for Oncology (ICD-O-3); Union for International Cancer Control (UICC 8th edition) clinical stage; date of diagnosis; dichotomous outcome for each type of surgery (open, laparoscopic or thoracoscopic), endoscopic treatment (endoscopic mucosal resection or endoscopic submucosal dissection for gastrointestinal cancers), chemotherapy including targeted therapy, hormone therapy and radiotherapy; and the starting date for each procedure. Also, use of other treatments (e.g., locoregional therapy for liver cancer, photodynamic therapy or immunotherapy) and non-invasive treatments including palliative care is recorded but without the starting date. Information on the treatment hospital, routes to diagnosis (e.g., screen-detected) and hospital (e.g., referral) is included in each record.
2.3. Study population
Records of patients diagnosed with primary cancer in the stomach (ICD-O-3: C16), colorectum (C18–20), lung (C32–34), breast (C50), cervix (C53), oesophagus (C15), liver (C22) or pancreas (C25) were extracted. We included patients aged 15–99, and diagnosed at any clinical stage except the ‘not applicable’ stage (e.g., neuroendocrine carcinoma). Records of patients who resided outside Osaka Prefecture or at an unknown address at the time of diagnosis were excluded. To avoid a double-counting of the patients who visited two or more hospitals separately for diagnosis and treatment, we excluded records from hospitals that did not start the first treatment. Records of patients diagnosed through autopsy and those with unknown routes to hospital were also excluded.
2.4. Statistical analysis
Firstly, we assessed the impact of potential factors associated with patient mobility at the population-averaged level. We estimated their effects in odds ratios (ORs) of receiving cancer care in the same SMA of residence. Generalised estimating equations (GEE) with a logit link [16] was used because some patients had multiple records per hospital due to multiple primaries in different or the same site (e.g., transverse and sigmoid colon). Both these records were retained regardless of the chronological order (i.e., synchronous or metachronous cancers). An exchangeable correlation structure was assumed for these records [16], [17], [18].
Age at diagnosis, sex, eight SMAs of residence, cancer site, clinical stage, type of first treatment and periods defined by DSE (before and after DSE) were included as the main exposures of interest based on clinical relevance. Type of hospital (prefectural or national DCCH), routes to diagnosis or hospital were candidates for the potential exposures. Potential exposures were selected using a directed acyclic graph (Appendix A). No serious multicollinearity among exposures was seen (Pearson’s correlation coefficients < 0.3).
Secondly, we identified which subgroup of patients was most affected by the COVID-19 pandemic in Osaka at the population-averaged level. Using the same multivariable model built in the first analysis, we further assessed whether the effect of the DSE on the odds of receiving care within the RA differed by subgroup. The interactions between DSE and two factors of interest (age and RA) were examined one at a time.
The date of diagnosis was divided into two periods: the ‘before DSE’ phase from 1 January 2019 to 6 April 2020; and the ‘after DSE’ phase from 7 April 2020 to 31 December 2020. Age was divided into four groups (15–54, 55–64, 65–79 and 80–99), routes to diagnosis into two (screen-detected and other routes) and routes to hospital into three (no referral or other routes, with a referral and established patient). For the type of first treatment, chemoradiotherapy was categorised into radiotherapy because radiotherapy is not necessarily offered at all prefectural DCCHs, while chemotherapy is offered at all DCCHs; the presence of full-time radiotherapy oncologists is not a requisite condition for the accreditation of a prefectural DCCH.
We conducted multiple imputation for routes to diagnosis (missing at <1%) and clinical stage (7%) (Appendix B) [19], [20]. Stata 16 MP [21], yED [22] and ArcGIS V.10.5 [23] were used.
3. Results
A total of 105,256 records for patients diagnosed with cancer in eight sites were identified in the CanReCO data. Following the exclusion criteria (Appendix C), 78,839 records among 74,312 patients were included in the analysis.
3.1. Baseline characteristics
The median age at diagnosis was 72 (interquartile range 62–79), and 51% were male ( Table 1). Over 30% of the patients resided in the City. Colorectal cancer was the most common (26%) of the eight cancer types. Around half of the patients were clinical stage 0/I. Most patients attended DCCHs through referral (65%), and 51% of the total went to prefectural DCCHs to receive care. Surgery was the first treatment procedure for 48% of the patients. Overall, 80% of the patients started the first treatment in their RA. The proportion of people receiving care within their RA increased as patients aged. It also varied by SMA: the lowest in Naka-Kawachi (61%) and the highest in the City (93%). The proportion increased as the stage progressed, from 78% in stage 0/I to 82% in stage IV. The DSE has led to an absolute increase of 0.8% in the proportion of patients receiving care within their own RA.
Table 1.
Baseline characteristics of patients diagnosed with cancer residing in Osaka Prefecture, Japan, 2019–2020.
| Total, n (%) | Imputed dataa(%) | % receiving care in own area | |
|---|---|---|---|
| Total number of records | 78 839 | 80.1 | |
| Age at diagnosis | |||
| Median (interquartile range) | 72 (62 to 79) | ||
| 15–54 | 11 987 (15.2) | 77.4 | |
| 55–64 | 10 293 (13.1) | 77.0 | |
| 65–79 | 39 341 (49.9) | 79.8 | |
| 80–99 | 17 218 (21.8) | 84.4 | |
| Sex | |||
| Male | 40 129 (50.9) | 79.3 | |
| Female | 38 710 (49.1) | 80.8 | |
| Residential areab | |||
| Osaka City | 24 619 (31.2) | 92.8 | |
| Toyono (N) | 8903 (11.3) | 78.9 | |
| Mishima (NNE) | 5890 (7.5) | 71.9 | |
| Kita-Kawachi (NE) | 8298 (10.5) | 74.9 | |
| Naka-Kawachi (E) | 8696 (11.0) | 60.7 | |
| Minami-Kawachi (SE) | 5866 (7.4) | 72.9 | |
| Sakai (S) | 8131 (10.3) | 74.5 | |
| Senshu (SW) | 8436 (10.7) | 85.0 | |
| Period defined by DSEc | |||
| Before DSE | 50 970 (64.7) | 79.8 | |
| After DSE | 27 869 (35.4) | 80.6 | |
| Cancer site | |||
| Stomach | 13 832 (17.5) | 79.9 | |
| Colorectum | 20 736 (26.3) | 82.5 | |
| Lung | 15 293 (19.4) | 79.6 | |
| Breast | 11 708 (14.9) | 81.6 | |
| Cervix | 4857 (6.2) | 78.9 | |
| Oesophagus | 3806 (4.8) | 69.4 | |
| Liver | 4158 (5.3) | 78.2 | |
| Pancreas | 4449 (5.6) | 78.9 | |
| UICC clinical stage | |||
| 0/I | 37 331 (50.9)d | (51.8) | 78.4 |
| II/III | 21 994 (30.0)d | (29.4) | 81.2 |
| IV | 14 069 (19.2)d | (18.8) | 81.7 |
| Missing | (5445 [6.9]) | NA | 82.7 |
| Type of hospital | |||
| Prefectural DCCH | 40 304 (51.1) | 85.2 | |
| National DCCH | 38 535 (48.9) | 75.0 | |
| Routes to diagnosis | |||
| Screen-detected | 12 258 (15.7)d | (15.7) | 79.7 |
| Other routes | 65 889 (84.3)d | (84.3) | 80.2 |
| Missing | (692 [0.9]) | NA | 71.5 |
| Routes to hospital | |||
| No referral or other routes | 8070 (10.2) | 89.0 | |
| Referral | 51 455 (65.3) | 78.7 | |
| Established patient | 19 314 (24.5) | 80.1 | |
| Type of first treatmente | |||
| Diagnosis, monitoring or palliative care only | 7363 (9.3) | 86.2 | |
| Endoscopic treatment | 14 709 (18.7) | 78.6 | |
| Surgery | 37 697 (47.8) | 80.4 | |
| Chemo- or hormone therapy | 13 138 (16.7) | 78.6 | |
| Radiotherapy or other treatments only | 5932 (7.5) | 77.3 |
Abbreviations: 95% CI, 95% confidence interval; DCCH, designated cancer care hospital; DSE, declaration of state of emergency; NA, not applicable; UICC, Union for International Cancer Control.
All variables in the table, hospitals and binary outcome of receiving care within the residential area were included in the imputation model.
Upper case letters in brackets after the area names represent directions from Osaka City.
Before DSE is defined as 1 January 2019 to 6 April 2020 and After DSE as 7 April 2020 to 31 December 2020.
The denominators of the percentages exclude records with missing stage/routes to diagnosis.
Other treatments only include locoregional therapy for liver cancer (e.g., radiofrequency ablation and chemoembolisation), photodynamic therapy and immunotherapy. Chemoradiotherapy was included in radiotherapy.
3.2. Factors associated with receiving care within the residence
The crude ORs of receiving care within the patient’s RA increased up to 1.56 for older age, female, more advanced stage, and patients receiving palliative care compared with each reference group ( Table 2). Among eight SMAs, the crude odds of receiving care within the RA were substantially higher than Naka-Kawachi as the reference in the following three areas: more than an eightfold increase in the City, twofold in Toyono and threefold in Senshu.
Table 2.
Univariable and multivariable generalised estimating equations for odds ratios of receiving care within residential area using imputed data, Osaka, Japan, 2019–2020.
| Univariable analysis | Multivariable analysis | |
|---|---|---|
| Crude ORs (95% CI) | Adjusted ORs (95% CI) | |
| Age at diagnosis | ||
| 15–54 | Reference | Reference |
| 55–64 | 0.98 (0.92 to 1.04) | 1.05 (0.98 to 1.13) |
| 65–79 | 1.15 (1.09 to 1.21) | 1.32 (1.24 to 1.40) |
| 80–99 | 1.56 (1.47 to 1.66) | 1.68 (1.56 to 1.80) |
| Sex | ||
| Male | Reference | Reference |
| Female | 1.09 (1.05 to 1.13) | 1.06 (1.02 to 1.11) |
| Residential areaa | ||
| Osaka City | 8.32 (7.78 to 8.89) | 8.41 (7.86 to 9.00) |
| Toyono (N) | 2.51 (2.34 to 2.69) | 2.34 (2.18 to 2.51) |
| Mishima (NNE) | 1.68 (1.56 to 1.81) | 1.66 (1.54 to 1.80) |
| Kita-Kawachi (NE) | 1.94 (1.81 to 2.07) | 1.97 (1.84 to 2.11) |
| Naka-Kawachi (E) | Reference | Reference |
| Minami-Kawachi (SE) | 1.75 (1.62 to 1.88) | 1.78 (1.65 to 1.91) |
| Sakai (S) | 1.96 (1.83 to 2.10) | 2.00 (1.86 to 2.14) |
| Senshu (SW) | 3.65 (3.39 to 3.94) | 3.41 (2.92 to 3.40) |
| Period defined by DSEb | ||
| Before DSE | Reference | Reference |
| After DSE | 1.03 (1.00 to 1.06) | 1.04 (1.00 to 1.07) |
| Cancer site | ||
| Stomach | 0.91 (0.88 to 0.93) | 0.90 (0.87 to 0.94) |
| Colorectum | Reference | Reference |
| Lung | 0.91 (0.88 to 0.94) | 0.88 (0.83 to 0.92) |
| Breast | 1.00 (0.95 to 1.04) | 1.12 (1.05 to 1.19) |
| Cervix | 0.88 (0.82 to 0.94) | 1.06 (0.97 to 1.16) |
| Oesophagus | 0.73 (0.70 to 0.77) | 0.71 (0.67 to 0.76) |
| Liver | 0.86 (0.82 to 0.90) | 0.84 (0.78 to 0.90) |
| Pancreas | 0.89 (0.84 to 0.93) | 0.86 (0.80 to 0.93) |
| UICC clinical stage (imputed) | ||
| 0/I | Reference | Reference |
| II/III | 1.10 (1.07 to 1.12) | 1.09 (1.05 to 1.12) |
| IV | 1.14 (1.10 to 1.18) | 1.15 (1.10 to 1.21) |
| Type of hospital | ||
| Prefectural DCCH | Reference | Reference |
| National DCCH | 0.49 (0.47 to 0.51) | 0.59 (0.56 to 0.61) |
| Routes to diagnosis (imputed) | ||
| Screen-detected | Reference | - |
| Other routes | 1.02 (0.99 to 1.06) | - |
| Routes to hospital | ||
| No referral or other routes | Reference | Reference |
| Referral | 0.58 (0.55 to 0.61) | 0.59 (0.54 to 0.61) |
| Established patient | 0.62 (0.59 to 0.65) | 0.60 (0.56 to 0.64) |
| Type of first treatmentc | ||
| Diagnosis, monitoring or palliative care only | 1.29 (1.23 to 1.35) | 1.18 (1.11 to 1.25) |
| Endoscopic treatment | 0.95 (0.92 to 0.97) | 0.95 (0.91 to 0.99) |
| Surgery | Reference | Reference |
| Chemo- or hormone therapy | 0.95 (0.91 to 0.98) | 0.95 (0.90 to 0.99) |
| Radiotherapy or other treatments only | 0.91 (0.87 to 0.95) | 0.91 (0.85 to 0.97) |
Abbreviations: 95% CI, 95% confidence interval; DCCH, designated cancer care hospital; DSE, declaration of state of emergency; NA, not applicable; ORs, odds ratios; UICC, Union for International Cancer Control.
Because routes to diagnosis were not considered to be associated with the outcome, it was excluded from the multivariable analysis.
Upper case letters in brackets after the area names represent directions from Osaka City.
Before DSE is defined as 1 January 2019 to 6 April 2020 and after DSE as 7 April 2020 to 31 December 2020.
Other treatments only include locoregional therapy for liver cancer (e.g., radiofrequency ablation and chemoembolisation), photodynamic therapy and immunotherapy. Chemoradiotherapy was included in radiotherapy.
Overall, the adjusted ORs (aORs) followed similar patterns to the crude ORs for most variables in both imputed data and complete cases (Table 2 and Appendix D). Because routes to diagnosis were not considered to be associated with the outcome, it was excluded from the multivariable analysis (Appendix A). Compared with patients with colorectal cancer as the reference, those with breast cancer had 1.12 times (95% confidence interval [CI] 1.05–1.19) higher adjusted odds of receiving care within their own area. In contrast, patients with stomach, lung, oesophageal, liver or pancreatic cancer tended to travel out of their area; the aORs ranged from 0.71 (95% CI 0.67–0.76) for oesophageal cancer to 0.90 (95% CI 0.87–0.94) for stomach cancer. Patients in national DCCHs, with a referral or established patients, had 0.6 times the odds of receiving care within their RA compared with those in prefectural DCCHs or without a referral. Patients who underwent endoscopic treatment, chemotherapy or radiotherapy as the first treatment tended to travel out of their RA slightly more than patients undergoing surgery as the reference, with the aORs ranging from 0.91 to 0.95. The DSE led to an aOR of 1.04 (95% CI 1.00–1.07).
3.3. Subgroups affected by COVID-19 and DSE
After the DSE, only patients aged 65–79 became more likely to remain within their RA than before the DSE (aOR 1.05, 95% CI 1.00–1.10), while patients aged ≤ 64 also showed similar patterns ( Fig. 2). The area-specific aORs increased after the DSE only in Kita-Kawachi (aOR 1.11, 95% CI 1.01–1.22) and Sakai (aOR 1.10, 95% CI 1.00–1.21). For other areas such as in Naka-Kawachi and Minami-Kawachi, the aORs were around 1.1, but the 95% CIs crossed 1. The results were similar in the complete case analyses (Appendix E).
Fig. 2.
Strata-specific adjusted odds ratios of receiving care within residential area after DSE (reference: before DSE), Osaka, Japan, 2019–2020. Abbreviations: 95% CI, 95% confidence interval; aOR, adjusted odds ratio; DSE, declaration of state of emergency; OR, odds ratio. Upper case letters in brackets after the area names represent directions from Osaka City. Before DSE is defined as 1 January 2019 to 6 April 2020 and After SED as 7 April 2020 to 31 December 2020. Error bars indicate 95% confidence intervals. Both two multivariable GEE models are adjusted for sex, residential area, cancer site, stage, type of hospital, routes to hospital and type of first treatment. An interaction term between DSE and age at diagnosis, and DSE and residential area is added for each model of age at diagnosis and residential area, respectively. The imputation model for each analysis includes all variables in the GEE multivariable model, indicator for 66 hospitals and the outcome, with an interaction term between DSE and each of age at diagnosis and residential area.
4. Discussion
All patient, tumour and treatment factors evaluated in this study were associated with whether or not patients received cancer care within their RA. There was a clear gradient for patients with older age (as reported in Western countries [24], [25], [26], [27]) and more advanced stage to stay within their RA. Also, females and breast cancer patients tended to remain within their RA. In the second analysis, we showed that patients aged ≤ 79 or in the East and South SMAs changed their behaviour to remain within their RA after the DSE.
Previous studies have restricted treatment to one type, such as surgery [28] or radiotherapy [26]. Therefore, it was not possible to compare the effect of different treatment procedures on patient mobility. In our study, which included a variety of treatment types, we were able to compare how patient mobility differed by procedure type; the aOR of receiving care within the patient’s RA was higher for those receiving palliative care only than those having surgery. This is understandable as palliative care is likely to be provided in the patient’s own community. Patients receiving chemotherapy or radiotherapy as the first treatment tended to travel outside their area. This may have been due to referral to hospitals with greater expertise if they required complex treatment strategies such as conversion therapy. Also, radiotherapy is not offered in all DCCHs for reasons mentioned earlier. Endoscopic treatment is not provided in all DCCHs because the patient volume for this procedure is not included in the requisite conditions for accreditation. Therefore, people receiving these treatments might be more likely to travel outside their own area than those having surgery.
Regarding cancer site, when crude and adjusted ORs were compared (Table 2), the change in direction and magnitude of ORs in breast and cervical cancer suggests a confounding effect between sex and cancer site. Because stomach cancer is the third most common cancer in Japan [29], the care might have been relatively decentralised (aOR 0.90, 95% CI 0.87–0.94) than in Western countries. Patients with oesophageal, liver, or pancreatic cancer tended to seek care outside their area. The situation is to be expected because surgeries for these cancers are considered high-risk [30], and the Government also facilitates the centralisation of care for refractory cancers [31]. In contrast, care for breast cancer may have been decentralised. Although they often require postoperative chemotherapy and radiotherapy, 77% of breast cancer patients underwent surgery as the first treatment. Unlike the factors above, patients receiving care in national DCCHs or with a referral may reflect their or their primary care doctor’s preference regarding referral patterns. They were likely to have travelled outside their RA through their own choice.
The COVID-19 pandemic has affected patient mobility. Overall, patients have become more likely to receive cancer care within their RA after the DSE (aOR 1.04, 95% CI 1.00–1.07). Even after adjusting for other factors, age (65−79) and SMAs in the East and South remained as subgroups that stayed within their RA after the DSE. During the early phase of the pandemic, COVID-19 mortality was reported to be high, particularly among older patients [32], and fear of infection could have been a strong driver for behavioural change among this population. The peak age of cancer incidence in Japan coincides with the most affected population, and the number of cancer diagnoses also declined among this age group [11]. The consequences of this delay in diagnosis due to COVID-19, in terms of cancer stage, treatment, place of treatment, and cancer survival, could be more substantial among this population.
Our study showed the complexity of patient mobility according to geographical area, which was also observed in London, England [33]. The reasons for travelling can be multifactorial, and determination of whether the observed differences in utilisation as the proxy measure of access are fair or unfair inequalities is beyond the scope of this study. Although we identified associated factors and their potential effect sizes, further investigation is needed to aid policymakers’ decision on whether intervention is needed in this situation. Linking our results to current resource inputs and clinical needs by SMA for efficiency issues, measures of access such as time from diagnosis to treatment by hospital, patients’ travelling time [34] or survival inequalities [33] for equity issues, may be necessary. It is also important to explore the reasons for travelling.
The differential effect of the DSE on patient mobility by SMA also needs further scrutiny. Because a low percentage of people in the East and South originally received care within their RA (Table 1), the effect of the DSE might have been more apparent there than in the City and North. Besides, unmeasured differences in population characteristics might partly explain the situation. For example, the Kita-Kawachi area is a commuter area [35] developed around the railway connecting the area to Osaka City. Although a relatively high proportion of people in this area initially received cancer care within their RA, patients who commute to their working place in the City and received cancer care near to their place of work might have changed their place of treatment to DCCHs in their own RA after the DSE.
The strength of this study is that we estimated the magnitude of the population-averaged effects of the potentially associated factors by GEE, using the data comprising more than 70 thousand records. There are limitations. Firstly, because patients in Japan have freedom to choose where they receive care, it is hard to conclude whether our results were due to access inequity that needs to be amended, or merely patients’ preference. However, mobility changes due to the pandemic gave us a ‘natural experiment’ opportunity from which to draw an inference: the reduced mobility after the DSE and relatively large effect size (aORs 0.6) among referred or established patients and those who go to national DCCHs, imply that patient mobility is likely to be largely influenced by patients’ choice. Secondly, we focused on changes in patient mobility within Osaka Prefecture in the second analysis. However, the change in the pattern of between-prefectural travel was likely to be more affected by the DSE.
To conclude, increasing age and stage were positively associated with odds of patients receiving cancer care within their RA. The COVID-19 pandemic and DSE might have particularly affected people aged ≤ 79 and residents in SMAs in the East and South. Linking our results with current inputs, needs or survival will be necessary to support policymakers’ decisions on whether the situation needs intervention.
Funding
This project is supported by the Osaka Cancer Prevention Fund (Kendai No. 2181) from the Osaka Prefectural Government, Japan. MKS is supported by JSPS Grant-in-Aid for Early-Career Scientists (JSPS KAKENHI Grant Number JP22K17340). The funders have no role in the study design, analysis, interpretation of the results, drafting or the decision to submit for publication.
CRediT authorship contribution statement
Conceptualisation, MKS, TM, CM, SK, IM; Data curation, MKS, TM, CM, SK; Formal analysis, MKS; Funding acquisition, MKS, TM, IM; Investigation, TM, IM; Methodology, MKS; Project administration, TM, IM; Resources, MKS, TM, CM, SK, IM; Software, MKS, TM, CM, SK; Supervision, TM, CM, SK, IM; Visualisation, MKS; Writing-original draft, MKS; Writing-review & editing; TM, CM, SK, IM. All authors had full access to data. MKS and IM had responsibility for the decision to submit for publication.
Declaration of Competing Interest
We declare no competing interests.
Acknowledgements
We would like to thank hospitals for providing data and participating in the CanReCO project. Ethics approval was obtained from the Institutional Review Board at the Osaka International Cancer Institute (approved number 21065). Patient consent for the CanReCO project was not required because data was collected for health policy planning and research use with an opt-out approach for DCCHs. The participating hospitals provided data under a confidentiality agreement that the data would not be publicly available. We would like to thank Dr Julia Mortimer for English language proofreading.
Footnotes
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jcpo.2023.100416.
Appendix A. Supplementary material
Supplementary material
.
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