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Researchers of chronic disease often gather data that are measured on a continuum rather than as a “present–absent” or “yes–no” dichotomy. Examples include the following: episodes of a symptom; number of sick days, cigarettes smoked, or alcoholic drinks consumed; measures of health care use, such as number of doctor visits or days of hospitalization; and costs incurred (in dollars). Such measures are referred to as “count” data; that is, the observations can have only nonnegative integer values (0, 1, 2, 3, . . . ). Such data are most often gathered during a specified period of time (eg, the past month or year). For some of these measures, most study participants may have a zero count (eg, no episode of a symptom, no cigarettes smoked, no use of health care services). These data are typically not normally distributed, and the positive skew in their distribution cannot be resolved by data transformation. The Centers for Disease Control and Prevention’s (CDC’s) health-related quality of life (HRQOL) Healthy Days measure (1) is an example of such count data.

The Behavioral Risk Factor Surveillance System (BRFSS) questionnaire includes an HRQOL section composed of 3 questions related to respondents’ healthy days. These questions ask respondents to report the number of days in the previous 30 days when 1) their physical health was not good, 2) their mental health was not good, and 3) poor physical or mental health kept them from doing their usual activities (2). Responses to the Healthy Days questions are count data because the response must be an integer. For each of the Healthy Days questions, most respondents report zero days (2), and most of the nonzero responses are concentrated in the left side of the distribution, producing a skewed distribution with large variance.

Two simple and familiar methods have often been used to analyze Healthy Days data. The first categorizes the data into 2 (eg, ≥14 vs <14 d) (3–6) or more (eg, 0 d, 1–13 d, and ≥14 d) categories (7). Although categorizing these data may simplify the statistical analyses, there may be drawbacks (8–12), including the loss of information and power (8,10,11). Categorization does not make use of within-category information, and all participants above or below a particular cut point are treated equally even though the outcome among participants within a particular category may vary significantly: for example, 1 bad mental health day in the previous 30 days is quite different from 12 bad days, even though 1 and 12 are both in the category of less than 14 days. In addition, the selection of cut points is often arbitrary, making it difficult to compare results among studies and hampering meta-analysis. Furthermore, categorizing a continuous variable may bias results (9,12).

The second most common method of analyzing the association between various risk factors and the number of reported physically and mentally unhealthy days uses linear regression models and keeps the outcome in its original scale of 0 to 30 days (13–15). These approaches often violate the assumption of normal distribution of errors, which can distort true relationships and render significance tests invalid (16,17). Several regression models are appropriate for analyzing count data, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression (18); however, they have not been used widely in analyzing Healthy Days data (19).

This study used data from the 12 states that included a question on homeownership in their 2009 BRFSS to examine the independent relationship between homeownership and number of mentally unhealthy days. Studies have shown that homeownership is associated with several health outcomes (20,21), but we are not aware of any study that has examined the relationship between homeownership and HRQOL. Our objective was to determine whether using different analytic methods produced different findings. We compared 5 multivariate regression models — logistic, linear, Poisson, negative binomial, and zero-inflated negative binomial — with respect to 1) how well the modeled data fit the observed data and 2) how model selections affect inferences.

Among adults aged 35 years or older, about four-fifths (79.3%) owned a home (Table 1). The mean number of mentally unhealthy days was 3.3 days and the median was 0 days, indicating a positive skew. An exact Poisson distribution having a mean of 3.3 days predicted that about 4% of the participants would have zero unhealthy days during the 30-day time frame. However, about two-thirds of individuals (66.8%) reported no mentally unhealthy days, indicating an excess of zeros. The variance was 58.7, which is much greater than the mean (3.3 d).

The logistic regression analysis found no significant association (P = 0.22) between homeownership and having 14 or more mentally unhealthy days in the previous month (Table 2). The parameter estimate (regression coefficient) of homeownership was −0.139 (adjusted odds ratio = 0.87, 95% confidence interval [CI], 0.70–1.09).

Both linear and Poisson regression models underestimated the percentage of nonoccurrence (0 days) and overestimated the percentage in the category 1 to 9 days (Figure 1). The parameter estimates (regression coefficients) of homeownership in these 2 models were not significantly different from zero (Table 2), indicating homeownership was not significantly associated with the number of mentally unhealthy days in either model.

Negative binomial regression resulted in a better fit of the data than did either linear or Poisson regression (Figure 2). The overdispersion parameter (α) in the negative binomial model was 7.2, which is significantly greater than zero (P < .001), indicating that the data were overdispersed. The likelihood-ratio test was 430,000 (P < .001), suggesting that negative binomial regression is preferred over Poisson regression. The parameter estimate of homeownership was −0.137 in the negative binomial model (Table 2) (ie, an adjusted rate ratio of 0.87 [exponential (−0.137)] [95% CI, 0.77–0.99]). Hence, homeowners had about 13% fewer mentally unhealthy days than nonowners (P = .04).

The zero-inflated negative binomial regression provided a better fit of the data than did negative binomial regression (Figure 2). The z value of the Vuong test was 42.5 (P < .001), confirming that the zero-inflated model fit the data better than the non-zero–inflated model. The parameter estimate in the logistic component of the model was 0.216 (P = .003) (Table 2); as such, we can interpret the estimate as an adjusted odds ratio of 1.24 [exponential (0.216)] (95% CI, 1.08–1.43). Hence, homeowners were 24% more likely than non-homeowners to have excess zero mentally unhealthy days. The parameter estimate in the negative binomial component of the model was −0.011 (P = 0.83) (ie, an adjusted rate ratio of 0.99 [exponential (−0.011)] [95% CI, 0.90–1.09]), suggesting no significant association between homeownership and the number of unhealthy days.

In studying the association between homeownership and CDC’s Healthy Days measure as an example, we demonstrated how different models can influence statistical inference — the process of drawing conclusions from empirical data. We did not find an independent association between homeownership and number of mentally unhealthy days by logistic, linear, or Poisson regression models. The negative binomial model showed that homeowners had a moderate but significantly lower number of unhealthy days than non-homeowners. The zero-inflated negative binomial model indicated an association between homeownership and whether individuals reported any mentally unhealthy days but not the number of unhealthy days.

We found that a zero-inflated negative binomial model fit the observed number of mentally unhealthy days reported in BRFSS data better than any of the other models we tested. Despite its ability to model count data, Poisson regression did not fully address the problem of overdispersion. Overdispersion may result in misleading inferences about regression parameters (18). Likewise, negative binomial regression may be less able than zero-inflated negative binomial regression to address the problem of excess zeros. We did not test all possible models in this study. Other models (eg, Hurdle regression, zero-inflated Poisson) can be used to model count data, and there are many methodological deviations of the models we applied (18). Researchers should ensure that their analytic methods fit the data and also use statistical techniques that lead to meaningful interpretations (25). For example, a researcher may find that a zero-inflated negative binomial distribution best fits the data but that a negative binomial distribution without the zero-inflation also meets all statistical assumptions and lends itself to more practical interpretations. In such cases, we advise that researchers consider parsimony and practical interpretation of a model when choosing an analytical method.

The main purpose of this data analysis was not to establish or affirm the “true” relationships between homeownership and number of mentally unhealthy days. We applied various models to BRFSS Healthy Days data as an example to illustrate the importance of appropriate model selection. The study has several limitations. First, it was based on self-reported data from 12 states that elected to include the social context module in its 2009 BRFSS. Second, the survey was conducted through telephone interviews; people without telephones and those who used only cell phones were excluded; these people may be less likely to be homeowners. Third, the BRFSS is a cross-sectional survey: information on the outcome measure (number of mentally unhealthy days) and characteristics (eg, homeownership) of the respondents were assessed at a single point in time. Hence, determining whether the association of characteristics with outcomes preceded or followed the outcomes was not possible.

Any statistical inference requires some assumptions, and incorrect assumptions can invalidate statistical inference (26). Some researchers may ignore the underlying assumptions of their statistical approaches or select a simpler or familiar method as long as the results support their hypothesis. These approaches go against the primary goal of observational epidemiology, which is to assess the detail, strength, direction, shape, and pattern of the relationships between exposures and outcomes. This goal cannot be accomplished without using appropriate statistical methods.

We believe that when the assumptions of analytic techniques are carefully matched to the nature of the data distribution, the results will be more accurate and compelling. False results can mislead researchers, the public, and policy makers and are potentially detrimental to public health. The selection of data analytic techniques is not a trivial statistical matter. Using appropriate analytic procedures will maximize the accuracy and utility of the findings on factors that are of great importance in clinical, policy, and fiscal decisions.