Variability in Use of Health Services and Its Association with Self-Management Skills: A Population-Based Exploratory Analysis

Abstract

Self-management skills are helpful in making appropriate health-related decisions; however, improvements in self-management skills do not always translate into changes in health services utilization. Therefore, to assess associations between self-management skills and health services use, a randomly selected sample of 984 residents was drawn from South East Queensland, Australia. This cross-sectional study collected self-reported data on respondents' use of health services, health-related behaviors, demographics, and 3 self-management skills: self-monitoring, health services navigation, and social mobilization. The results indicate that the ability to navigate the health system was associated with greater use of health services while the ability to mobilize one's social supports was associated with reduced use of allied health services. Being able to navigate the health system appeared to be driven by necessity, in that those with higher navigation skills were unemployed, financially stressed, or had a chronic condition. This pattern of results confirms the socioeconomic gradient that exists in health. (Population Health Management 2016;19:31–38)

Background

The increasing prevalence of chronic conditions has been identified as a key factor in placing demand on health services and requires active patient management to contain the cost of health care. The notion that patients are passive recipients of care has been challenged for some time and active involvement by patients in maintaining and achieving health has been on the agenda to achieve better health outcomes. Therefore, self-management education has been identified as a key contributing factor to reducing unnecessary use of health services.¹ However, supporting self-management for better health outcomes is an ongoing process, not a time-limited education course or intervention. Self-management is described as “enabling individuals to make informed choices, to adapt new perspectives and generic skills that can be applied to new problems as they arise and to maintain or regain emotional stability.”² It is also an individual's ability to engage in behavioral change as a result of self-monitoring in collaboration with health care professionals and through the mobilization of adequate supports.³ Brady identified 3 main skills that underpin most self-management education, namely: action planning, self-monitoring, and ability to seek social support.⁴ These skills are thought to increase an individual's ability to obtain, understand, and act on basic health information and make health-related decisions.⁵

However, improvements in self-management skills do not always translate into changes in the utilization of health services.⁶ The relationship between health service utilization and self-management skills is complex and poorly understood,^7,8 and discrepant findings have emerged with some studies showing reductions in health service usage and others demonstrating increased health service usage as people tend to address their health concerns more proactively.⁹ It is possible that these discrepant findings are associated with the fact that self-management is a multidimensional construct consisting of multiple skills, each of which may have a different relationship with health service usage. For example, the ability to monitor one's own symptoms may be associated with higher levels of health service use because of greater levels of preventive health management awareness. In contrast, the ability to mobilize supports in one's own environment may result in less reliance on the health system. However, the ability to negotiate the health system and successfully engage with health professionals may be associated with higher levels of usage, but a more targeted use of services. Unfortunately, there is scant research into how different self-management skills might affect health care usage. This is truer for populations without chronic illness, as a large proportion of interventions in self-management concentrate on chronic illness.¹⁰ The population health approach suggested by Rose¹¹ indicates that small but positive changes in health-related self-management skills (rather than chronic disease focused self-management skills) on a large scale will possibly drive a positive shift in parameters of health and the use of health care.

As health care resources are finite, information about the ways in which specific self-management skills might impact the use of health services can help to develop effective strategies to improve health. Therefore, this paper investigates the relationship between 3 self-management skills (ie, to monitor one's own health, navigate the health system, and/or mobilize supports in one's social environment) and patterns of health service usage via an analysis of data collected from South East Queensland, Australia. The current study hypothesizes that each self-management skill can predict variability in the use of health care services.

Methods

Study design and participants

A randomly selected sample of 984 residents was drawn from an urban region in the outer southern suburbs of South East Queensland, Australia. According to the Australian Bureau of Statistics, this region is an area of relative social disadvantage. Ten thousand households were randomly selected from 29 suburbs from the city council's property database. Between March and May 2009, selected households were mailed a questionnaire as well as information about the project, a consent form, an entry form for a competition to win $500, and a postage-paid reply envelope. If no response had been received 1 month after the requested return date, a follow-up letter and questionnaire were sent. Approximately 8000 surveys were successfully delivered to the randomly selected addresses and 1004 people returned a questionnaire (13% response rate). Only 1 response was requested per household and only responses from households within the designated area could be included. After exclusion of incomplete surveys, the final sample consisted of 974 participants (12.17% response rate). This response rate is consistent with similar survey research using cold contact mailings or telephone calls in socially disadvantaged general populations.^12,13 The sample is representative of the area population on most measures, including a standardized measure of social and economic disadvantage,¹⁴ employment status,¹⁴ and income.¹⁵ The study was approved by Griffith University Human Research Ethics Committee.

Measures

The self-administered survey (available from the authors upon request) focused on the respondents' health and well-being, and included questions regarding chronic disease management and treatment, accessibility and quality of health services, health-related attitudes, health-related behaviors, and demographic details such as sex, age, education, marital status, employment status, residential area, and birth country. Data also were collected on financial stress based on whether or not respondents were paying more than half their gross income on mortgage or rent. Information on health behaviors included individuals' smoking status, alcohol intake, amount of exercise, as well as fruit and vegetable intake. Self-reported information on height and weight was collected along with the number and type of chronic diseases, and the year of diagnosis. Perceived health status was measured using the General Health scale of the SF-36v2 Health Survey International Version, Standard Form¹⁶ using Australian norms.¹⁷ To collect information on the use of health services, respondents indicated the number of unplanned hospital admissions, emergency visits to the hospital, number of visits to their general practitioner (GP), and visits to allied health care professionals in the previous year. Seventeen items from the Health Education Impact questionnaire (HeiQ), developed by Osborne et al, were used to collect information about individuals' attitudes toward managing their own health.¹⁸ The HeiQ is an Australian-developed health education impact evaluation system. It consists of 51 questions, organized into a set of 8 scales. Each scale is an independent questionnaire and together the 8 scales provide a comprehensive profile of self-management skills.

Three scales that were most relevant to the outcomes of this study were used for this research:

1. Self-monitoring and insight (7 items): This scale assesses individuals' ability to monitor their condition and their physical/emotional responses that lead to appropriate self-management action. Higher scores represented higher levels of insight into the process of living with a health condition, the ability to self-monitor, and the ability to set reasonable health targets.

2. Health services navigation (5 items): This scale assesses individuals' understanding of and ability to interact with a range of health organizations and health professionals in order to meet their health needs. It also measures their confidence and ability to communicate and negotiate with health care providers. Higher scores characterize greater confidence in the ability to communicate and greater understanding of ways to access health care when needed.

3. Social mobilization (5 items): This scale assesses the ability to engage in social activities and mobilize essential support through interaction with others. Higher scores indicate higher levels of social interaction and support from others in response to health-related needs.

Each item contained a 5-point Likert scale, on which respondents indicated their strength of agreement.

Statistical analysis

The 3 self-management skills (self-monitoring and insight, health services navigation, and social mobilization) were summarized as a mean score for each individual (ie, total of responses to each item in that scale divided by number of items in the scale). To describe health services usage data, a short background on the functioning of the Australian health care system is required. The Australian health care system consists of both public and private institutions. The public health system (ie, Medicare) is funded by the Australian government and provides help with medical expenses and hospital care. Medicare ensures that all Australians have access to free or low-cost medical and hospital care while being free to choose private health services. Medicare mainly supports general practice and specialist visits, pathology services, optometric assessments and the pharmaceutical benefits scheme (PBS). Private health coverage can help to cover care that is not covered by Medicare, as well as payment for items such as hospital accommodation, operating room fees, allied health services, preventive health care, dentistry, some nursing care, and some medicines. There are other government benefits for disadvantaged populations, such as a health care or concession card that can provide income-dependent access to highly subsidized health care services and some less expensive medicines. Therefore, having private health insurance indicates affordability and access to a range of health services.

Health service usage data were clustered into 3 categories according to the type of service as detailed in the following list:

1. Medical Primary Care System: This system included GPs, pharmacists, and specialists, who form the core medical system in Australia. GPs act as gatekeepers; access to specialist care is available only with GP referral. A total of 731 participants had visited this service system in the preceding 12 months.

2. Allied Health System: This system included podiatrists, dietitians, optometrists, dentists, nutritionists, physiotherapists, occupational therapists, and/or speech therapists. These health professionals extensively support the medical primary system. A total of 671 participants visited this service system in the preceding 12 months.

3. Patient Education and Well-Being System: This system supports patients beyond the previous 2 systems and includes psychologists, social workers, and community-based nursing services. A total of 242 participants visited 1 or more of the practitioners included in this system in the preceding 12 months.

Data were further summarized to create a categorical variable for each participant to indicate whether or not he or she had used each system in past 12 months: yes [1] or no [0]. To test the association between the use of health services and various self-management skills, an independent sample t test was used to analyze the difference between the mean for each independent variable. Linear regression also was used to adjust for the confounding effect of other variables in the model. Significance was set at P<0.05 for all analyses.

Results

Overall, 71.2% of participants who responded to the survey were female. There was a greater response from individuals between 40 and 64 years of age (53.6%), current workers (57.7%), and married participants (56.2%). Just over half the sample (52.9%) had 1 or more chronic conditions; 57.1% had visited a GP in the past 1 year; and 60.6% lived within 5 km of the general practice they had visited. In terms of access to health care, 46.3% of the participants held private health coverage and 37.7% were entitled to a health or concession card.

Correlation between self-management skills and demographic/health-related factors

1. Self-Monitoring and Insight

As shown in Table 1, participants who used Medical Primary Care System had significantly better self-monitoring skills compared to those who did not (4.44 versus 4.28, P<0.05). Use of the Patient Education and Well-Being System was not associated with skills to monitor one's health. Individuals with chronic conditions were better at self-monitoring than those without chronic conditions (P<0.05), which was expected. Higher self-monitoring scores also were associated with the presence of preventive behaviors, such as being a nonsmoker, undertaking sufficient physical activity, eating sufficient servings of fruits and vegetables, and maintaining a normal body mass index. As shown in Table 2, females compared to males (P<0.05) and older participants compared to younger participants (P<0.05) were significantly better at self-monitoring and understanding their health. Having financial stress was associated with lower levels of self-monitoring (P<0.05) but education, employment status, and private health insurance did not show any impact on self-monitoring scores. Therefore, self-monitoring was a skill that was mainly associated with frequent use of health services related to chronic illness or related to awareness of preventive health behaviors.

Table 1.

Association Between Various Health Indicators, Use of Health Services, and Self-Management Skills

		Self-Monitoring			Health Services Navigation			Social Support Mobilization
	N	Mean (SD)	t (df)	P	Mean (SD)	t (df)	P	Mean (SD)	t (df)	P
Health Systems Usage
Medical Primary Care System
Visited	731	4.44 (0.55)	3.75 (949)	0.00	4.40 (0.642)	5.65 (949)	0.00	3.95 (1.04)	1.59 (949)	0.11
No visits	220	4.28 (0.59)			4.11 (0.717)			3.83 (1.08)
Patient Education and Well-Being System
Visited	242	4.37 (0.58)	1.13 (969)	0.25	4.29 (0.679)	1.38 (969)	0.16	3.79 (1.07)	2.33 (969)	0.02
No visits	729	4.42 (0.56)			4.36 (0.664)			3.97 (1.02)
Allied Health System
Visited	671	4.44 (0.54)	2.59 (969)	0.01	4.39 (0.641)	3.49 (969)	0.00	3.90 (1.06)	1.18 (969)	0.23
No Visits	300	4.34 (0.61)			4.23 (0.714)			3.99 (0.98)
Health Indicators
Chronic Condition
Yes	506	4.45 (0.54)	2.68 (969)	0.00	4.40 (0.619)	2.89 (969)	0.00	3.84 (1.13)	2.93 (969)	0.00
No	465	4.36 (0.59)			4.27 (0.713)			4.03 (0.92)
Smoking
Nonsmoker	779	4.46 (0.54)	5.80 (964)	0.00	4.38 (0.64)	3.92 (964)	0.00	3.99 (1.01)	3.86 (964)	0.00
Current Smoker	187	4.19 (0.64)			4.17 (0.74)			3.67 (1.14)
Alcohol
Low Risk	900	4.40 (0.56)	0.25 (969)	0.79	4.33 (0.66)	1.05 (969)	0.29	3.93 (1.03)	0.02 (969)	0.98
High Risk	71	4.42 (0.61)			4.42 (0.69)			3.93 (1.13)
Physical Activity
Sufficient	651	4.45 (0.53)	3.14 (969)	0.00	4.37 (0.63)	2.39 (969)	0.02	4.01 (0.98)	3.25 (969)	0.00
Insufficient	320	4.33 (0.62)			4.27 (0.72)			3.77 (1.14)
Vegetable and Fruit Intake
Sufficient	428	4.53 (0.51)	5.94 (969)	0.00	4.44 (0.61)	4.11 (969)	0.00	4.09 (0.99)	4.31 (969)	0.00
Insufficient	543	4.31 (0.59)			4.26 (0.70)			3.80 (1.06)
Body Mass Index
Within normal range (≤24.99kg/m²)	330	4.48 (0.50)	2.86 (925)	0.00	4.34 (0.64)	0.20 (925)	0.84	4.06 (0.97)	2.81 (925)	0.00
Overweight or obese (≥25 kg/m²)	597	4.36 (0.59)			4.34 (0.71)			3.85 (1.02)

df, degrees of freedom; SD, standard deviation.

Table 2.

Association Between Demographic Factors and Self-management Skills

		Self-Monitoring			Health Services Navigation			Social Support Mobilization
	N	Mean (SD)	t (df)	P	Mean (SD)	t (df)	P	Mean (SD)	t (df)	P
Demographics
Sex
Female	691	4.43 (0.53)	2.36 (969)	0.02	4.35 (0.62)	0.89 (969)	0.37	3.97 (1.01)	1.94 (969)	0.05
Male	280	4.34 (0.64)			4.31 (0.76)			3.83 (1.10)
Age
≤49 years	460	4.28 (0.59)	6.99 (969)	0.00	4.18 (0.68)	6.99 (969)	0.00	3.84 (0.97)	2.56 (969)	0.01
≥50 years	511	4.52 (0.51)			4.48 (0.62)			4.01 (1.09)
Marital Status
Single	312	4.41 (0.55)	0.04 (967)	0.96	4.30 (0.69)	1.17 (967)	0.24	3.67 (1.23)	5.44 (967)	0.00
Couple	657	4.41 (0.57)			4.36 (0.65)			4.05 (0.92)
Socioeconomic determinants
Education
Degree	191	4.43 (0.49)	0.73 (969)	0.46	4.36 (0.61)	0.59 (969)	0.55	3.88 (1.02)	0.69 (969)	0.49
No degree	780	4.40 (0.58)			4.33 (0.68)
Employment
Employed	560	4.37 (0.56)	2.18 (969)	0.03	4.29 (0.69)	2.92 (969)	0.00	3.91 (0.98)	0.60 (969)	0.54
Not working	411	4.45 (0.57)			4.41 (0.62)			3.95 (1.11)
Financial Stress
Yes	181	4.25 (0.65)	3.97 (953)	0.00	4.37 (0.67)	3.08 (953)	0.00	3.66 (1.14)	3.94 (953)	0.00
No	774	4.44 (0.54)			4.20 (0.66)			3.99 (1.01)

Df, degrees of freedom; SD, standard deviation.

2. Health Services Navigation

As expected, participants who used Medical Primary Care System and Allied Health System scored higher in terms of their ability to navigate health systems (Table 1). Once again, having a chronic condition was associated with higher scores for being able to navigate the health system. Ability to navigate the health system was higher with being a nonsmoker and having sufficient intake of fruits and vegetables, but did not differ with alcohol use, physical activity, or body mass index (Table 1). Although the unemployed, those experiencing financial stress, or older participants reported a better ability to navigate the health system, having better education did not impact this ability and sex had no impact on being able to navigate the system better (Table 2).

3. Social Mobilization

Participants who did not use the Patient Education and Well-Being System scored higher on social mobilization (Table 1). Participants who used the Medical Primary Care or Allied Health systems did not differ in the social mobilization skill when compared to nonusers of those systems. Being a smoker, or being overweight or obese reduced an individual's ability to engage socially. Those who mobilized their social supports also were more likely to engage in adequate physical activity and fruit or vegetable intake. No differences in sex or age were observed for the participants' social engagement (Table 2). However, being married was strongly associated with the ability to mobilize social supports. Those who were able to mobilize social supports were those who did not experience financial stress and did not have a chronic condition.

Assessing the impact of self-management skills and other factors on the “use of health care services”

As shown in Table 3, after controlling for all other variables assessed for their associations with self-management skills, odds of using the Medical Primary Care System were significantly higher among individuals who had better health services navigation skills, while self-monitoring or social support mobilization did not significantly affect use of Medical Primary Care System. Interestingly, being able to navigate the system increased the odds of using Allied Health services (OR=1.48, CI 1.09–2.00), while self-monitoring did not impact this use (OR=0.94, CI 0.65–1.35) and having social support actually showed a reduction in the odds of using Allied Health services (OR=0.69, CI 0.56-0.85). None of these 3 skills had significant impact on the use of Patient Education and Well-Being System. Although nonsmokers, individuals drinking alcohol at low risk levels, individuals without private health insurance, and individuals with financial stress showed significantly reduced odds for use of the Allied Health System, the younger population showed significantly higher odds of using the Allied Health System. Greater use of Medical Primary Care System was associated with sufficient physical activity but greater use of the Patient Education and Well-Being System was associated with having a chronic condition. Use of the Patient Education and Well-Being System was lower in individuals in the younger age group and individuals without private health insurance. Other factors did not show significant association with the use of any of the health systems (Table 3).

Table 3.

Results from Multivariate Analysis Predicting Use of the Health Care System

	Medical Primary System		Patient Education and Well-Being System		Allied Health System
	OR	CI	OR	CI	OR	CI
Self-Management Skills
Self-monitoring	1.06	0.71–1.57	0.99	0.69–1.43	0.94	0.65–1.35
Health services navigation	1.49	1.10–2.05^*	0.96	0.71–1.31	1.48	1.09–2.00^*
Social Support Mobilization	0.93	0.74–1.16	0.84	0.70–1.02	0.69	0.56–0.85^*
Demographics
Sex
Male	1		1		1
Female	1.10	0.72–1.66	0.76	0.52–1.12	0.73	0.50–1.06
Age
≥50 years	1		1		1
≤49 years	0.94	0.63–1.42	0.50	0.34–0.73^*	2.10	1.43–3.07^*
Socioeconomic determinants
Employment
Not working	1		1		1
Employed	1.25	0.84–1.84	1.41	1.01–1.98^*	1.23	0.87–1.73
Financial Stress
Yes	1		1		1
No	1.31	0.82–2.01	0.95	0.63–1.44	0.66	0.45–0.98^*
Private Health Insurance
No	1		1		1
Yes	1.01	0.69–1.45	0.66	0.47–0.92^*	0.35	0.25–0.50^*
Health Indicators
Smoking
Current Smoker	1		1		1
Nonsmoker	1.17	0.73–1.87	0.72	0.46–1.12	0.52	0.35–0.77^*
Alcohol
High Risk	1		1		1
Low Risk	0.59	0.32–1.14	1.22	0.68–2.23	0.46	0.26–0.81^*
Physical Activity
Insufficient	1		1		1
Sufficient	1.52	1.02–2.27^*	0.96	0.67–1.34	0.85	0.61–1.98
Chronic Condition
No	1		1		1
Yes	1.36	0.91–2.03	1.55	1.07–2.23^*	0.96	0.67–1.37

P<0.05 after regression analysis that included use of health services as an outcome variable while self-management skills and all other variables in Tables 1 and 2 are independent variables. Only significant results are included in this table.

CI, confidence interval; OR, odds ratio.

Discussion

The primary results from this research clearly indicate that each self-management skill is associated with using health services differently. Although being able to navigate health services is predictive of higher use of the Medical Primary Care System and Allied Health services, being able to mobilize social supports predicts lower use of Allied Health services. Use of the Patient Education and Well-Being System is not predicted by any of the 3 self-management skills; however, having a chronic condition is predictive of use of this system.

At an individual level, there also appeared to be significant differences between those with higher levels of the 3 self-management skills. Financial stress was associated with all 3 skills, but in different ways. Specifically, experiencing financial stress was associated with reduced ability to self-monitor and mobilize support, but also associated with increased ability to navigate the health system. Having a chronic condition was associated with skills in self-monitoring and health services navigation, but not in mobilizing social supports. This finding is not surprising, but leaves people with chronic conditions vulnerable to social isolation. It is also possible that having a chronic illness means there is a certain level of dependency on the health system, which in turn improves navigation skills because of frequent use of the system.

The observation that being a smoker or being overweight or obese reduced the individual's ability to engage socially may indicate that these individuals lack self-esteem related to the realization that smoking or weight gain is not a socially accepted norm. Literature on social norms treats social networks as an exogenous variable, and in the case of smoking, indicates that smokers are more likely to be members of different social networks than nonsmokers.¹⁹ It is established that continuing and nondirective social support exerts the greatest effect on quit attempts; therefore, lack of ability to engage socially needs to be addressed as a strategy to reduce smoking.²⁰ As smoking prevalence is higher among disadvantaged groups,²¹ this relationship between smoking and inability to engage socially impacts socioeconomic gradient in health. Similarly, large and persistent social inequalities related to education level and socioeconomic status contribute to obesity and overweight in many countries.²² Reduced ability to engage socially for overweight or obese individuals might further impact negatively on their capacity to seek support. Using these 2 examples, it can easily be seen that an individual's ability to engage socially and society's views toward established norms is a multitudinous cycle. For the betterment of disadvantaged individuals, understanding and addressing these associations must be a part of strategies that promote the public good.

In the Australian health system, primary care is heavily weighted as a first point of contact, with a focus on encouraging the use of health services such as screening programs and empowering individuals to monitor their own health. To a certain extent, these results indicate that the current system is successful in achieving this goal as there was a significant association between use of the Medical Primary Care System and being able to navigate health services. Self-monitoring ability was strongly associated with higher levels of preventive behaviors, results that are supported by previous research indicating an increase in screening for breast cancer and prostate cancer after self-monitoring interventions in individuals.⁵ However, in this study self-monitoring was a skill that depended on the absence of financial stress and being employed, positioning it as an ability of those who were in a privileged position. An equally important skill was the ability to mobilize one's social supports. This skill had a positive association with all health behaviors and outcomes, an expected result given that social support has repeatedly been shown to have a positive health impact across multiple contexts.^4,23 Again, the finding that this skill was higher in those without financial stress confirms the importance of economic status as a determinant of health.²⁴ Being married supported an individual to mobilize other supports, which then feeds into a self-perpetuating cycle of being supported hence seeking support and feeling less isolated. Clearly, there is a role for the Patient Education and Well-Being System to supplement the support for those who are less able to mobilize their own supports. Ironically, having a chronic condition was associated with reduced capacity to mobilize supports; however, being able to mobilize social supports predicted decreased use of allied health services. This specifically shows the importance of social supports for people with chronic conditions and that it can possibly lead to reduction in the use of allied health care services.

Being able to navigate the health system appeared to be a self-management skill driven by necessity, in that those who reported higher levels of this skill were those with chronic conditions, unemployment, and financial stress. This pattern of results also confirms the socioeconomic gradient that exists in health care and disease management.²⁵ It is possible that this group of economically disadvantaged and chronically unwell people spend more time learning how to negotiate the health system and, in doing so, show higher use of the Medical Primary Care and Allied Health systems. Although this needs to be assessed in future studies, the situation places them in a potentially damaging downward spiral of dependence on the health system and possibly reduced responsibility for their own health and inability to establish support networks. It is precisely this spiral that self-management initiatives seek to avoid.

Limitations

It is important to note that the current study cannot draw any causal conclusions. The relationships identified in this analysis could occur in multiple directions. Nevertheless, important patterns emerged that confirm the multidimensional nature of self-management and the different roles played by its components. As the current paper is based on secondary analysis, limited data were available to establish various associations. The study was conducted in a relatively disadvantaged region, perhaps influencing the importance of socioeconomic variables. In this population, almost 50% of the sample reported having 1 or more chronic conditions, which is a high rate of disease particularly given that almost half the individuals were younger than 50 years of age. National data collected at a similar time²⁶ found that nearly all people aged 65 years and older reported having at least 1 long-term condition (with more than 80% of people in this age group having 3 or more long-term conditions). These data identify that a high prevalence of chronic conditions at a much younger age in this study population might be related to socioeconomic disadvantage.

The study by House et al suggested that young persons of lower socioeconomic status experience a degree of health impairment similar to that of older persons of higher socioeconomic status.²⁷ The use of self-reported data might be a limitation; however, the sample was representative of the area population on most measures, including a standardized measure of social and economic disadvantage,¹⁴ employment status,¹⁴ and income.¹⁵ The study performed the analysis regardless of illness status, which can be a point of disagreement. However, if the focus on a population-wide approach to prevention, as advocated by Rose,^11,28 is to be applied to self-management skills, then it would suggest that everyone should possess self-management skills. Having high levels of self-management skills will potentially allow individuals to make appropriate decisions and eventually develop the self-efficacy required to take action to implement health-related changes regardless of their disease status. Finally, nonresponse bias may affect this study's results; however, the response rate is consistent with similar survey research using cold contact mailings or telephone calls in socially disadvantaged general populations and the sample is representative of the population in a disadvantaged area.

Conclusion

This study is based on a large, randomly selected sample from a socially disadvantaged urban region on the southern outskirts of the capital city. The results showed that different components of self-management were associated with different patterns of service use; more importantly, the results demonstrated that people with chronic conditions, financial stress, and unemployment are even more disadvantaged by the nature of their self-management skills. By necessity, they engage in learning how to navigate health systems and rely heavily on those health systems, yet have poorer outcomes. Those who can monitor their own health are likely to have less financial stress, make better use of the primary care system, and report better health behaviors and outcomes. These findings clearly demonstrate the way in which negative and positive spirals can occur economically disadvantaged and chronically unwell people spend more time learning how to negotiate the health system rather than concentrating on self-monitoring or mobilizing supports. This situation places them in a potentially damaging downward spiral of dependence on the health system and reduced ability to care for their own health.

Implications for behavioral health

Efficient use of health services requires active user engagement in the management of health and the ability of the end user to seek help when required. Although self-management and use of health services has been studied extensively in the population with chronic illness, this research indicates that, as an overall population health approach, improving self-management skills to modify behaviors that are associated with making health care decisions on the individual level should be integrated into routine care as a strategy for prevention.

Footnotes

Author Disclosure Statement

Drs. Parekh, Kendall, and Ehrlich declared no conflicts of interest with respect to the research, authorship, and/or publication of this article. The authors received no financial support for the research, authorship, and/or publication of this article.

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