Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning
Abstract
:1. Introduction
2. Methods
2.1. Survey Data Collection and Pre-Processing
2.1.1. Survey Design
2.1.2. Geocoding
2.1.3. Built Environment Measures
2.1.4. Perceived Built Environment and Accessibility
2.1.5. Quality of Life
2.1.6. Other Key Variables
2.2. Predictive Modeling for Quality of Life of Cancer Patients
3. Results
3.1. Descriptive Statistics and Factor Analysis
3.2. Predictive Modeling Results
4. Discussion
5. Conclusions
- Our findings regarding the effects of built environment features such as density and access to healthcare facilities on the QoL of cancer patients indicate that a supportive built environment can overcome the barriers in the outdoor environment, increase the likelihood of physical activity, and therefore improve perceived quality of life. These results point out that urban design and transportation planning need to become more friendly for this population group with particular needs and requirements.
- To improve social equity, it is fundamental to design environments compatible with the needs of all community groups, including people who are struggling with chronic diseases that require ongoing medical attention or limit activities of daily living in the long term.
- Understanding the associations between built environment and health-related QoL can help in the development of intervention policies that aim to improve cancer patients’ wellbeing. Hence, there is a need for collaboration between transit agencies, MPOs, and community planners to target the living environment and mobility needs of people who are burdened with chronic disease. To this end, urban and transportation planners and practitioners should be more involved in this field and acquire more knowledge from other disciplines. Integrating transportation planning with public health and social studies could reinforce existing policies and strategies in transportation accessibility and equity and therefore increase wellbeing and QoL.
- In addition, there is an inherent need to develop a QoL measurement that comprehensively counts for subjective feelings as well as objective factors in terms of patients’ health condition, transportation, and built environment. This QoL measurement can be used as a policy tool by communities and local governments to evaluate the extent to which the mobility and built environment meet the needs of patients with chronic diseases.
- The inverse associations between population density and cancer patients’ QoL indicate that compact development strategies can be fulfilled when policymakers address the side effects of urban density, such as fear of crime, high noise, and traffic congestion. This compact development pattern should concentrate on strategies that increase robust transportation options and improve public health indicators such as air quality while creating safe and secure neighborhoods that preserve more open space.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Nomenclature
QoL | Quality of Life |
IRB | Institutional Review Board |
GIS | Geographic Information System |
ACS | American Community Survey |
EI | Entropy Index |
KMO | Kaiser–Meyer–Olkin |
MLP | Multi-layer Perceptron |
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Variables | Description | ||||
---|---|---|---|---|---|
Count | Percent | Mean | S.D. | ||
Socio-demographic attributes | |||||
Gender | Female | 292 | 49.6 | ||
Male | 297 | 50.4 | |||
Race | White | 510 | 86.6 | ||
Non-white | 79 | 13.4 | |||
Education | Well-educated (bachelor and above) | 316 | 53.7 | ||
Less-educated (below bachelor) | 256 | 43.5 | |||
Missing | 17 | 2.9 | |||
Employment status | Employee | 220 | 37.4 | ||
Not-employee | 364 | 61.8 | |||
Missing | 5 | 0.8 | |||
Residential status | Owner | 219 | 37.2 | ||
Not-owner | 366 | 62.1 | |||
Missing | 4 | 0.7 | |||
Number of cars in the household | 0 | 45 | 7.6 | ||
1 | 243 | 41.3 | |||
2 | 217 | 36.8 | |||
3 or more | 84 | 14.3 | |||
Health insurance | Medicaid | 96 | 16.3 | ||
Medicare | 208 | 35.3 | |||
Affordable Care Act | 21 | 3.6 | |||
Employer-paid insurance | 142 | 24.1 | |||
Private health insurance | 54 | 9.2 | |||
Uninsured | 30 | 5.1 | |||
Other insurance | 37 | 6.3 | |||
Missing | 1 | 0.2 | |||
Income | 50,872 | 28,132 | |||
Age | 53 | 15.58 | |||
Household Size | 2.55 | 1.36 | |||
Built environment characteristics | |||||
Population density | 3714 | 6761 | |||
Entropy index | 0.66 | 0.04 | |||
Intersection density | 172 | 96 | |||
Transit stop density | 12 | 20 | |||
Distance to transit (min) | 27.29 | 95.23 | |||
Travel distance to the closest large hospital (min) | 12 | 206 | |||
Perceptions | |||||
Perceived built environment | 99.72 | 25.33 | |||
Perceived accessibility | 92.99 | 15.16 | |||
Health-related variables | |||||
Cancer type (diagnosis) | Easy | 285 | 48.4 | ||
Intermediate | 203 | 34.5 | |||
Hard | 72 | 12.2 | |||
Unknown | 29 | 4.9 | |||
Cancer treatments | |||||
Radiotherapy | 1 = having radiotherapy | 266 | 45.2 | ||
0 = not having radiotherapy | 323 | 54.8 | |||
Chemotherapy | 1 = having chemotherapy | 273 | 46.3 | ||
0 = not having radiotherapy | 316 | 53.7 | |||
Other | 1 = having other treatment | 261 | 44.3 | ||
0 = not having radiotherapy | 328 | 55.7 | |||
Quality of life | |||||
Overall quality of life | Terrible | 17 | 2.9 | ||
Poor | 67 | 11.4 | |||
Average | 168 | 28.5 | |||
Good | 219 | 37.2 | |||
Excellent | 118 | 20 |
Please Indicate How Well Your Residence and Its Location Meet the Following Characteristics | Loadings | |
Perceived built environment | Easy access to your health provider | 0.788 |
Easy access to drugstores | 0.797 | |
Closeness to work/school | 0.772 | |
Closeness to family members who can take care of me when I need them | 0.730 | |
Affordable neighborhood according to income and treatment costs | 0.805 | |
Quiet, safe, and secure neighborhood according to mental and physical condition | 0.735 | |
Please indicate the approximate travel distance (in minutes) from your current residence to the following errands | Loadings | |
Perceived accessibility | Closest public transit station | 0.517 |
Closest gas station | 0.846 | |
Closest restaurant/fast-food place | 0.905 | |
Closest drugstore | 0.896 | |
Closest grocery store | 0.889 | |
Patients’ primary health provider | 0.584 |
Logistic Regression | Decision Tree | Random Forest | MLP | |
---|---|---|---|---|
F-score | 0.71 | 0.70 | 0.69 | 0.72 |
AUROC | 0.64 | 0.67 | 0.63 | 0.66 |
Feature Name | Importance Score | |
---|---|---|
1 | Age | 0.14198 |
2 | Travel distance to closest large hospital | 0.12031 |
3 | Perceived accessibility | 0.11206 |
4 | Distance to transit (min) | 0.10921 |
5 | Population density | 0.09243 |
6 | Health insurance | 0.08123 |
7 | Entropy index | 0.07142 |
8 | Education (well-educated) | 0.03246 |
9 | Number of cars in the household | 0.02834 |
10 | Transit stop density | 0.02341 |
11 | Cancer treatments (chemotherapy) | 0.02240 |
12 | Employment status (employee) | 0.01907 |
13 | Cancer type (diagnosis) | 0.01650 |
14 | Gender | 0.01106 |
15 | Cancer treatments (radiotherapy) | 0.00730 |
16 | Race (white) | 0.00659 |
17 | Cancer treatments (other) | 0.00292 |
Variables | Coef | St. Error | Z | p-Values |
---|---|---|---|---|
Socio-demographic attributes | ||||
Gender (female) | 0.2621 | 0.230 | 1.141 | 0.254 |
Race (white) | −0.0448 | 0.315 | −0.142 | 0.887 |
Education (well-educated) | 0.6215 | 00.213 | 2.922 | 0.003 *** |
Employment status (employee) | 0.2489 | 0.231 | 1.078 | 0.281 |
Number of cars in the household | 0.2589 | 0.394 | 0.657 | 0.511 |
Health insurance | −0.7557 | 0.381 | −1.984 | 0.047 *** |
Age | 1.8632 | 0.523 | 3.565 | 0.000 *** |
Built environment characteristics | ||||
Population density | −14.1817 | 7.153 | −1.983 | 0.047 *** |
Entropy index | −0.2651 | 0.629 | −0.421 | 0.673 |
Transit stop density | −0.4187 | 0.706 | −0.593 | 0.553 |
Distance to transit (min) | −2.2074 | 1.367 | −1. 614 | 0.106 |
Travel distance to closest large hospital | 1.6386 | 1.127 | 1.453 | 0.146 |
Perceptions | ||||
Perceived accessibility | −1.1933 | 0.774 | −1.543 | 0.123 |
Health-related variables | ||||
Cancer type (diagnosis) | 0.1053 | 0.308 | 0.342 | 0.732 |
Cancer treatments (radiotherapy) | 0.0180 | 0.251 | 0.072 | 0.943 |
Cancer treatments (chemotherapy) | −0.7943 | 0.263 | −3.021 | 0.003 *** |
Cancer treatments (other) | −0.3485 | 0.299 | −1.164 | 0.244 |
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Etminani-Ghasrodashti, R.; Kan, C.; Arif Qaisrani, M.; Mogultay, O.; Zhou, H. Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning. Sustainability 2021, 13, 5438. https://doi.org/10.3390/su13105438
Etminani-Ghasrodashti R, Kan C, Arif Qaisrani M, Mogultay O, Zhou H. Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning. Sustainability. 2021; 13(10):5438. https://doi.org/10.3390/su13105438
Chicago/Turabian StyleEtminani-Ghasrodashti, Roya, Chen Kan, Muhammad Arif Qaisrani, Omer Mogultay, and Houliang Zhou. 2021. "Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning" Sustainability 13, no. 10: 5438. https://doi.org/10.3390/su13105438
APA StyleEtminani-Ghasrodashti, R., Kan, C., Arif Qaisrani, M., Mogultay, O., & Zhou, H. (2021). Examining the Impacts of the Built Environment on Quality of Life in Cancer Patients Using Machine Learning. Sustainability, 13(10), 5438. https://doi.org/10.3390/su13105438