Whereas some have suggested that life chaos may be an important determinant of behavior and health,5
very little is known about it. Among children, chaos in the home has been measured using a 15-item scale and is associated with lower socioeconomic status, worse emotional adjustment, delayed cognitive development, and more behavioral problems.6,11,12
Previous studies have not directly measured chaos in adults. However, some research has implied that chaos may influence the health and health care of adults, having looked at issues of overcrowding, noise, and other similar factors.13,14
A qualitative study of HIV-infected adults found poverty is associated with life “instability” and a “sense of uncertainty.”5
With the goal of measuring instability, organization, the ability to anticipate the future, and to plan ahead, we developed a six-item instrument, which demonstrated acceptable construct validity and internal consistency reliability in a sample of disadvantaged persons with HIV. To reduce the possibility of endogeneity, we excluded specific environmental and personal factors that might cause life chaos, such as noise, overcrowding, unemployment, divorce, mental disorders, or barriers to care. This brief measure of global life chaos does not attempt to distinguish between potential subtypes of chaos (e.g., home, work, or psychological). Nonetheless, the psychometric properties of our measure suggest that the scale items capture a measurable construct of global life chaos that is related to, but different from, the associated constructs of unmet needs, mental health status, and other potential stressors.
Consistent with our hypothesis that having better social support and fewer stressors would lead to less chaos, having a partner or spouse and having no unmet needs were associated with less chaos. Contrary to our hypothesis, chaos did not vary by race/ethnicity, income and education or stressors, such as alcohol and drug use, homelessness, and having children. One possible reason for this lack of variation may be that our sample was fairly homogeneous with respect to sociodemographic and other characteristics.
Regarding health care use, persons with greater chaos were more likely to miss two or more visits in the 6 months before the baseline survey. Chaos was not associated with outpatient care at 6- or 12-month follow-up, although the direction and magnitude of the associations at follow-up were the same as at baseline. Some endogeneity bias might partly explain the association between chaos and health care use at baseline. Specifically, some subjects’ recent experience getting to their medical visits might have influenced their response to the chaos items asking about their ability to make appointments and keep a schedule.
Although chaos was unassociated with physical health status, chaos was strongly associated with worse mental health status. Given the nature of the study design and the fact that mental health status was unchanged over the study period, we cannot determine whether greater chaos leads to worse mental health status or whether worse mental health status leads to greater life chaos. Of course, the relationship between chaos and mental health status may be bidirectional. Future studies following cohorts over longer time periods and measuring chaos and mental health status at multiple time points might help tease out the direction of the relationship.
Our study lacks generalizability given our sampling methods targeting persons with HIV who receive inadequate care and should be tested in other populations. Items 3 and 4 of our scale attempt to measure slightly different components of chaos (stability vs predictability). But if these two items continue to perform as similarly in other populations, one of these items could be eliminated to make the instrument shorter. Perhaps, in other populations, including those that are more diverse and those with other diseases, chaos might be more strongly associated with health care use and health status, and we might see a stronger association between socioeconomic variables and chaos.
Whereas the follow-up rate in the study exceeded 75% at both follow-up surveys, our analysis of health care use and health status at follow-up might have been biased because persons with more chaos were more likely to have been nonresponders. We used attrition weights to account for this difference in follow-up rate, but these weights may not have fully accounted for the potential attrition bias.
The present study is the first that we are aware of to measure chaos in adults and to examine its association with health care use and health status. Our newly developed chaos measure demonstrated acceptable construct validity and internal consistency reliability, and its relationship with demographic characteristics, health care use, and health status was consistent with many of our hypotheses. Life chaos may be another important barrier to getting adequate care and may be a target for future interventions. Potential interventions might use reminders or other strategies to help patients be more organized, teach patients certain coping strategies to better deal with stressors, and use behavioral interventions to increase daily routines. Researchers should also consider the impact of noise, overcrowding, and other environmental factors on chaos and whether these factors might be additional targets for interventions. Finally, future clinical studies might consider measuring and adjusting for chaos when looking at health care use as an outcome.