Co-occurrence of diabetes and depression in the U.S.

Evidence exists that depression interacts with physical illness to amplify the impact of chronic conditions like diabetes. The co-occurrence of these two conditions leads to worse health outcomes and higher healthcare costs. This study seeks to understand what demographic and socio-economic indicators can be used to predict co-occurrence at both the state and the individual level. Diabetes and depression are modeled as a bivariate normal distribution using data from the Behavioral Risk Factor Surveillance System 2016-2017 cohorts. The tetrachoric (latent) correlation between diabetes and depression is 17.2% and statistically significant, however the likelihood of any person being diagnosed with both conditions is small-as high as 4.3% (Arizona) and as low as 2.3% (Utah). We find that demographic characteristics (sex, age, and race) operate in opposite directions in predicting diabetes and depression diagnosis. Behavioral indicators (BMI≥30, smoking, and exercise); and life outcomes, (schooling attainment, marital and veteran status) work in the same direction to produce co-occurrence and as such are more powerful predictors of co-occurrence than demographic characteristics. It is important to have a rapid and efficient instrument to diagnoses co-occurrence. Simple questions about lifestyle choices, educational attainment and family life could help bridge the gap between primary care and psychological services with beneficial spillovers for patient-doctor communication.

Introduction

During 2013–2016, 8.1% of Americans aged 20 and over reported having a depressive symptom in a given 2-week period [1]. Some people with MDD never get diagnosed, either because they do not seek care or because they are misdiagnosed. Depression is a common mental health disorder and there is growing evidence that is also significantly under-diagnosed. [2,3]. 9.4% of Americans have been diagnosed with diabetes in 2016 [4]. Just like depression, diabetes prevalence is also anticipated to grow, with estimates suggesting that the proportion of the population affected by diabetes will at least double by 2050 [5]. Currently, there is little funding on diabetes prevention yet, 1 in 4 health care dollars is spent to combat diabetes and its consequences [6]. While we lack accurate information about total and per capita cost of depression, there is evidence that comparatively, little funding has been available historically to combat depression and mental health in general. On average, states spend approximately 2% of their health dollars to the broad spectrum of mental health problems [7].

Meta-analyses have shown that depression is twice as prevalent among persons with diabetes as it is among persons without diabetes [8]. Depression has substantial economic repercussions when concurrent with other chronic illnesses. A study using data from the 2004–2011 Medical Expenditure Panel Survey (MEPS), a nationally representative estimate of healthcare expenditures, showed that the average medical cost for patients with diabetes and no depression was $10,016 (95% CI 9589–10,442), and with symptomatic depression was $20,105 (95% CI 18,103–22,106) [9]. The burden of this co-occurrence goes beyond the health care system. For example, depression is associated with a fourfold increase in 20+ days of reduced household work [10]. Evidence exists that depression interacts with physical illness to amplify the impact of other conditions and vice versa [11]. It increases adverse health behaviors like obesity, sedentary lifestyle, and substance abuse, which are risk factors for cardiovascular disease [12,13]. Depression is also associated with increased mortality post-myocardial infarction (Cox model hazard ratio for 6-month mortality associated with depression: 5.74 (95% CI: 4.61–6.87) p = .0006 [14]). Depression leads to decreased self-care and adherence to medical treatment, which adversely influences expectations of efficacy of treatment by reducing cognitive functioning and memory and reducing energy to exercise and follow regimens like checking blood glucose. Compared to nondepressed patients, patients with depression are significantly more likely to be non-adherent with medical treatment recommendations [15].

Because of all the above reasons, managing the co-occurrence of depressive disorders and chronic diseases, like diabetes, seems vital to health care delivery. Despite the growing recognition of the prevalence and importance of depressive disorders, there is little research examining factors that can predict the co-occurrence of depression with specific major chronic conditions and on how this knowledge could be used for prevention and treatment of populations at risk.

The goal of this study is to evaluate demographic and socio-economic indicators associated with both depression and diabetes at the macro (across-states) and micro level (individuals) by examining the likelihood of having:

depression with diabetes

depression without diabetes

diabetes without depression

The policy-relevant questions this exercise seeks to answer are twofold: (1) what demographic and socioeconomic characteristics can help us identify state-level hotspots, i.e., are there relevant differences in the probability of being diagnosed with diabetes and depressions across states? And (2) can we help profile who might be more likely to experience co-occurrence of diabetes and depressions to target resources in a more targeted manner and in accordance to needs?

Methods

Data

This analysis uses publicly released data from the Behavioral Risk Factor Surveillance System (BRFSS), representing the 2016–2017 cohort. BRFSS is a representative health survey from non-institutionalized civilian population aged ≥18 which allows for state-level prevalence estimates for both diabetes and depression [16]. The survey also collects information data on demographic characteristics.

The BRFSS uses disproportionate stratified sampling for landline calls and random sampling of cell phones. When accounting for the survey’s sampling and weighting, the BRFSS data are meant to be representative of the adult population residing in all 50 states, including the District of Columbia. The survey design was specified as follows, using STATA 15: svyset [pweight = _LLCPWT], strata(_STSTR) psu(_PSU) [17].

Subjects

The 2016–2017 cohort comprises 477,665 respondents across 50 states and the District of Columbia. For 9% of this cohort, we do not have complete information on the outcomes and modifiers of interest. The final sample used in the analysis comprises 436,744 responders.

The following question identified individuals with depression in BRFSS: Has a doctor, nurse, or other health professionals ever told you that you have a depressive disorder, including depression, major depression, dysthymia, or minor depression?

The following question identified individuals with diabetes in BRFSS: Has a doctor, nurse, or other health professionals ever told you that you have diabetes? Women who were told they had diabetes only during pregnancy were excluded from the sample.

Statistical approach

Depression and diabetes are both conditions that exist within a continuum and for which a diagnosis represents a dichotomous clinical adjudication, based on a discretizing threshold derived from an underlying latent variable. In the case of diabetes, the underlying latent variable would be fasting plasma glucose levels or glycated hemoglobin percentages and in the case of depression, it would be number of days a person experience loss of enjoyment, feelings of hopelessness or worthlessness and other related symptoms. The idea of a disease continuum underlying diagnosis informs the choice to model diabetes and depression as a bivariate normal distribution. All regression analyses control for state, sex, age, race, marital status, education, veteran status and lifestyle (exercise and smoking).

Each condition can be defined as a latent variable:

The goal is to estimate the coefficients needed to account for this joint distribution.

The latent correlation between diabetes () and depression () can be defined as:

Where is the tetrachoric correlation between and . It can be interpreted here as the correlation between the underlying diagnostic factors (before the application of thresholds) for diabetes and depression.

Results and discussion

The tetrachoric correlation () between diabetes and depression is 17.2% and statistically significant. Older adults are more likely to be diagnosed with diabetes, but after the age of 65, they are less likely to be diagnosed with depression. Males are more likely to be diagnosed diabetes but less likely to be diagnosed depression. All races and ethnicities other than white Caucasians are more likely to be diagnosed with diabetes and less likely to be diagnosed with depression. Native Americans have a higher prevalence for the co-occurrence of both conditions, compared to all other groups. Marital status, veteran status, school attainment, and exercise are the most important factors linked to the probability of having both diabetes and depression and operate in the same direction for both diabetes and depression (Table 1).

Table 1

Bivariate diabetes and depression model coefficients.

	diabetes					depression
Variables	Coef.	SE	P>|t|	95% CI (Lower, Upper)		Coef.	SE	P>|t|	95% CI (Lower, Upper)
Male	4.6%	0.012	< .0001	2.2%	7.0%	-45.4%	0.010	< .0001	-47.5%	-43.4%
non-Hispanic white	(omitted)
non-Hispanic black	26.2%	0.017	< .0001	22.8%	29.7%	-31.5%	0.018	< .0001	-35.0%	-28.0%
Hispanic	17.3%	0.021	< .0001	13.3%	21.4%	-35.2%	0.018	< .0001	-38.8%	-31.7%
Asian	33.3%	0.045	< .0001	24.5%	42.0%	-48.7%	0.044	< .0001	-57.3%	-40.0%
Native	35.1%	0.036	< .0001	27.9%	42.2%	0.0%	0.033	0.949	-6.4%	6.5%
Other	11.9%	0.034	< .0001	5.0%	18.8%	8.1%	0.031	0.007	2.1%	14.2%
Age 18 to 24	(omitted)					(omitted)
Age 25 to 29	12.7%	0.051	0.009	2.8%	22.6%	0.1%	0.024	0.808	-4.7%	4.8%
Age 30 to 34	38.7%	0.048	< .0001	29.3%	48.1%	0.0%	0.024	0.768	-4.8%	4.8%
Age 35 to 39	62.6%	0.045	< .0001	53.8%	71.4%	-0.2%	0.025	0.768	-5.2%	4.8%
Age 40 to 44	84.1%	0.044	< .0001	75.6%	92.6%	0.8%	0.026	0.516	-4.4%	6.0%
Age 45 to 49	104.4%	0.042	< .0001	96.1%	112.7%	3.2%	0.026	0.089	-1.9%	8.2%
Age 50 to 54	119.9%	0.041	< .0001	112.0%	127.8%	0.6%	0.024	0.452	-4.1%	5.4%
Age 55 to 59	137.1%	0.041	< .0001	129.1%	145.1%	3.4%	0.024	0.059	-1.4%	8.1%
Age 60 to 64	150.3%	0.041	< .0001	142.5%	158.2%	2.7%	0.025	0.110	-2.2%	7.5%
Age 65 to 69	165.5%	0.041	< .0001	157.6%	173.4%	-9.6%	0.025	0.001	-14.6%	-4.6%
Age 70 to 74	170.4%	0.042	< .0001	162.3%	178.5%	-21.9%	0.027	< .0001	-27.1%	-16.6%
Age 75 to 79	167.8%	0.044	< .0001	159.3%	176.3%	-38.5%	0.032	< .0001	-44.8%	-32.2%
Age 80 or older	157.1%	0.044	< .0001	148.6%	165.6%	-52.6%	0.032	< .0001	-58.8%	-46.4%
Veteran	7.0%	0.016	< .0001	3.8%	10.2%	14.7%	0.016	< .0001	11.6%	17.8%
BMI≥30	44.9%	0.000	< .0001	42.3%	47.4%	15.1%	0.000	< .0001	13.2%	17.1%
Exercise	-24.8%	0.012	< .0001	-27.2%	-22.4%	-22.7%	0.011	< .0001	-24.8%	-20.5%
Daily smoker	2.7%	0.019	0.089	-1.0%	6.3%	52.0%	0.014	< .0001	49.2%	54.7%
Occasional smoker	-8.4%	0.027	0.004	-13.8%	-2.9%	43.3%	0.020	< .0001	39.4%	47.2%
Former smoker	6.7%	0.012	< .0001	4.3%	9.2%	27.7%	0.012	< .0001	25.4%	30.0%
Never smoker	(omitted)					(omitted)
Married	(omitted)					(omitted)
Divorced	6.2%	0.017	< .0001	3.0%	9.4%	34.5%	0.014	< .0001	31.8%	37.1%
Widowed	10.3%	0.019	< .0001	6.7%	13.9%	21.8%	0.019	< .0001	18.2%	25.5%
Separated	9.2%	0.034	0.006	2.6%	15.8%	43.0%	0.028	< .0001	37.5%	48.5%
Never married	11.0%	0.019	< .0001	7.3%	14.7%	27.2%	0.015	< .0001	24.3%	30.1%
Member of an Unmarried couple	-0.2%	0.038	0.732	-7.5%	7.2%	23.6%	0.024	< .0001	18.8%	28.3%
Did not graduate from high school	37.8%	0.020	< .0001	33.9%	41.7%	20.3%	0.018	< .0001	16.8%	23.9%
Graduated high school	20.5%	0.014	< .0001	17.8%	23.3%	0.9%	0.013	0.387	-1.6%	3.3%
Some college	18.1%	0.014	< .0001	15.2%	20.9%	8.3%	0.012	< .0001	6.0%	10.6%
Graduated college	(omitted)					(omitted)
Constant	-2.85	0.047	< .0001	-2.94	-2.77	-0.93	0.028	< .0001	-0.98	-0.88
ρ	17.2%	0.008	< .0001	0.157	0.188

(omitted) flags the references categories have been omitted. ρ is the tetrachoric correlation between diabetes and depression.

The marginal effects reported in Table 2 are based on the joint probability of diabetes and depression. Because we have two outcomes of interest, overall, we have four joint probabilities. Table 2 reports three out of four joint probabilities and not P(y1 = 0, y2 = 0) because the marginal effects, across the four joint probabilities, sum up to zero. Probabilities have been computed holding covariates at the population mean. Table 2 shows that most demographic characteristics, sex, age, and race operate in opposite directions in predicting diabetes and depression. Behavioral indicators, i.e., BMI, smoking, and exercise; and life outcomes, i.e., schooling, marital and veteran status, on the other hand, work in the same direction to produce co-occurrence.

Table 2

Marginal effects for the joint probabilities.

	diabetes and depression P(y1 = 1, y2 = 1)				diabetes only P(y1 = 1, y2 = 0)				depression only P(y1 = 0, y2 = 1)
Variables	dy/dx	P>|t|	[95% Conf. Interval]		dy/dx	P>|t|	[95% Conf. Interval]		dy/dx	P>|t|	[95% Conf. Interval]
Male	-0.79%	< .0001	-0.87%	-0.70%	1.37%	< .0001	1.1%	1.6%	-9.78%	< .0001	-10.21%	-9.35%
non-Hispanic black	-0.02%	0.777	-0.14%	0.10%	3.88%	< .0001	3.4%	4.4%	-6.45%	< .0001	-6.99%	-5.92%
Hispanic	-0.29%	< .0001	-0.41%	-0.18%	2.70%	< .0001	2.2%	3.2%	-6.95%	< .0001	-7.50%	-6.40%
Asian	-0.28%	0.038	-0.54%	-0.02%	5.53%	< .0001	4.0%	7.0%	-8.65%	< .0001	-9.60%	-7.70%
Native	1.17%	< .0001	0.79%	1.56%	4.53%	< .0001	3.4%	5.7%	-1.17%	0.074	-2.45%	0.12%
Other	0.56%	0.001	0.24%	0.87%	1.09%	0.006	0.3%	1.9%	1.43%	0.038	0.08%	2.78%
Age 25 to 29	0.37%	0.028	0.04%	0.70%	1.37%	0.020	0.2%	2.5%	-0.35%	0.503	-1.39%	0.68%
Age 30 to 34	1.28%	< .0001	0.87%	1.69%	4.91%	< .0001	3.5%	6.4%	-1.27%	0.013	-2.27%	-0.26%
Age 35 to 39	2.29%	< .0001	1.82%	2.76%	9.21%	< .0001	7.5%	10.9%	-2.34%	< .0001	-3.34%	-1.34%
Age 40 to 44	3.40%	< .0001	2.84%	3.97%	13.84%	< .0001	11.9%	15.8%	-3.22%	< .0001	-4.20%	-2.24%
Age 45 to 49	4.72%	< .0001	4.08%	5.36%	18.88%	< .0001	16.8%	21.0%	-3.96%	< .0001	-4.88%	-3.04%
Age 50 to 54	5.33%	< .0001	4.71%	5.96%	23.01%	< .0001	20.9%	25.1%	-5.18%	< .0001	-6.00%	-4.37%
Age 55 to 59	6.65%	< .0001	5.95%	7.35%	28.04%	< .0001	25.7%	30.4%	-5.85%	< .0001	-6.65%	-5.05%
Age 60 to 64	7.36%	< .0001	6.63%	8.09%	32.11%	< .0001	29.8%	34.4%	-6.72%	< .0001	-7.48%	-5.97%
Age 65 to 69	6.83%	< .0001	6.11%	7.56%	39.11%	< .0001	36.7%	41.6%	-8.99%	< .0001	-9.60%	-8.39%
Age 70 to 74	5.69%	< .0001	5.02%	6.36%	42.94%	< .0001	40.3%	45.6%	-10.29%	< .0001	-10.83%	-9.76%
Age 75 to 79	3.91%	< .0001	3.26%	4.56%	44.27%	< .0001	41.4%	47.1%	-11.33%	< .0001	-11.80%	-10.85%
Age 80 or older	2.50%	< .0001	2.00%	2.99%	41.36%	< .0001	38.5%	44.3%	-11.90%	< .0001	-12.34%	-11.46%
Veteran	0.54%	< .0001	0.40%	0.68%	0.38%	0.024	0.1%	0.7%	3.11%	< .0001	2.37%	3.85%
BMI≥30	1.44%	< .0001	1.35%	1.52%	3.94%	< .0001	3.7%	4.2%	2.05%	< .0001	1.65%	2.46%
Exercise	-1.35%	< .0001	-1.46%	-1.23%	-2.12%	< .0001	-2.4%	-1.8%	-4.28%	< .0001	-4.78%	-3.79%
Daily smoker	1.31%	< .0001	1.12%	1.50%	-0.96%	< .0001	-1.3%	-0.6%	13.33%	< .0001	12.51%	14.15%
Occasional smoker	0.64%	< .0001	0.40%	0.87%	-1.64%	< .0001	-2.0%	-1.2%	11.48%	< .0001	10.31%	12.66%
Former smoker	0.82%	< .0001	0.71%	0.92%	0.06%	0.630	-0.2%	0.3%	6.13%	< .0001	5.57%	6.69%
Divorced	1.01%	< .0001	0.85%	1.17%	-0.19%	0.219	-0.5%	0.1%	8.17%	< .0001	7.45%	8.90%
Widowed	0.85%	< .0001	0.67%	1.03%	0.55%	0.006	0.2%	0.9%	4.75%	< .0001	3.83%	5.68%
Separated	1.42%	< .0001	1.03%	1.81%	-0.17%	0.580	-0.8%	0.4%	10.71%	< .0001	9.08%	12.33%
Never married	0.96%	< .0001	0.80%	1.11%	0.49%	0.011	0.1%	0.9%	5.88%	< .0001	5.17%	6.59%
Member of an Unmarried couple	0.51%	0.001	0.21%	0.80%	-0.53%	0.121	-1.2%	0.1%	5.61%	< .0001	4.36%	6.87%
Did not graduate from high school	1.93%	< .0001	1.69%	2.17%	3.95%	< .0001	3.4%	4.5%	3.19%	< .0001	2.34%	4.03%
Graduated high school	0.61%	< .0001	0.50%	0.72%	2.18%	< .0001	1.9%	2.5%	-0.41%	0.124	-0.93%	0.11%
Some college	0.71%	< .0001	0.59%	0.82%	1.70%	< .0001	1.4%	2.0%	1.27%	< .0001	0.76%	1.78%

The derivative of y with respect to x (dy/dx) represent the marginal probabilities. Exercise is defined as a binary response to the question: During the past month, other than your regular job, did you participate in any physical activities or exercises such as running, calisthenics, golf, gardening, or walking for exercise?

Fig 1 shows that depression alone is more prevalent than diabetes across all states. The vertical and horizontal axes show the average marginal probability across states of having either a diabetes or depression diagnosis, respectively. Thirteen states (Alaska, Arizona, D.C., Florida, Illinois, Louisiana, Missouri, Mississippi, Montana, Ohio, Oklahoma, Tennessee, and Wisconsin) have a prevalence of diabetes and depression exceeding the national marginal averages (13.5% for diabetes and 17.6% for depression are slightly higher than the national prevalence for each condition). Within these states, the probability of any one person being diagnosed with both conditions P(y1 = 1, y2 = 1) is as high as 4.3% (e.g., Arizona). Ten states (Arkansas, Delaware, Georgia, Hawaii, Maryland, New Hampshire, New Jersey, South Carolina, Texas, and Wyoming) have a higher prevalence of diabetes than the national average but also a reported diagnosed depression prevalence below the national average. Thirteen states (Alabama, Kansas, Kentucky, Massachusetts, Michigan, Nebraska, New Mexico, Nevada, Oregon, Pennsylvania. Rhode Island, South Dakota, and Virginia) have a higher prevalence of depression than the national average but also a prevalence of diabetes below the national average. The remaining 15 states have a lower reported prevalence of diabetes and depression than the national average. Within these states, the probability of any one person being diagnosed with both conditions (joint probability) is as low as 2.3% (e.g., Utah).

Fig 1

Marginal probabilities of diabetes and depression across states.

The vertical axis shows the state’s average marginal probability of being diagnosed with diabetes and the horizontal axis shows the state’s average marginal probability of being diagnosed with depression.

The vertical and horizontal lines in Fig 2 show the unconditional mean of diabetes and depression (10.7% and 16.6%, respectively) as well as the 0.5 probability threshold for each condition (black lines). Point A (coordinates 0.661, 0.531) represents a Native American woman between the ages of 65 and 69, with BMI≥30, an occasional smoker, who did not exercise in the 30 days prior to being surveyed, did not finish high school and is divorced. Point B (coordinates) represents a Native American woman between the ages of 55 and 59, with BMI≥30, a daily smoker, who did not exercise in the past 30 days, did not finish high school, and is divorced. Point C (coordinates 0.610, 0.659) represents a Native American woman between the ages of 55 and 59, with BMI≥30, a daily smoker, who did not exercise in the past 30 days, did not finish high school, widowed. Point D (coordinates 0.532, 0.613) represents a woman of a non-pre-specified race, between the ages of 60 and 64, with BMI≥30, daily smoker, who did not exercise, did not finish high school, and is widowed. To highlight the fact that behavioral variables are more important than demographic variables, Fig 2 shows in green individuals that have a riskier demographic profile (non-white females) compared to white males but that at the same time have the most protective socio-economic indicators, i.e., they are non-veterans, with a BMI<30, never smoked, currently married, exercise, and finished college. Highlighted in red are the opposite: white men with riskier socio-economic profiles—veterans, with a BMI≥30, that smoke occasionally or daily, that are unmarried, that do not exercise and that did not finish college. The average person in the sample with BMI<30, never smoked, currently married, exercise, and finished college has 1.3% (CI:1.1%- 1.5%) probability of having both diabetes and depression, while an otherwise comparable person with the opposite lifestyle has a probability of 16.1% (CI: 14.6%- 17.6%).

Fig 2

Predicting probabilities of diabetes and depression at the patient level.

The vertical axis and horizontal axes shows the patient’s probability of being diagnosed with diabetes and depression, respectively. Green dots represent non-veterans, non-white females, with a BMI<30, never smoked, currently married, exercise, and finished college. Red dots represent white men, veterans, with a BMI≥30, smokers, unmarried, and without a college degree.

Conclusions

While the probability of a single condition, irrespective of the other (i.e. the marginal probability) is relatively high for both conditions, the probability of being diagnosed with both conditions is currently very low, approximately 3%. This is problematic because the latent correlation of diabetes and depression is at least 5 times higher (17.2%). The reason for this discrepancy could be due to a systematic under-diagnosed of particularly vulnerable and at-risk individuals. It is difficult to combat diseases when the number of underdiagnosed cases is high. Estimates report that around two-thirds of all cases of depression are undiagnosed [16] and that approximately one-third of all cases of diabetes in the U.S. are undiagnosed [18].

The U.S. Preventive Services Task Force (USPSTF) now recommends screening for depression in the general adult population and notes that persons with chronic illnesses, such as diabetes, are at increased risk of developing depression. The USPSTF recommends screening should have adequate systems in place to ensure accurate diagnosis, effective treatment, and appropriate follow-up, albeit it does not consider the costs of providing this service in its assessment [19]. Effective care for depression in patients with diabetes is challenging. Mental health services are often separated from primary care leading to poor access to psychological services and other effective treatments. Primary care is an important entry point for diagnosis but primary care providers’ time is also limited. Harrison et al. (2010) found that females have higher odds of being screened for depression compared to males and that individuals ≥ 65 years of age are significantly less likely to be screened for depression than 40–64 years olds [20]. There is also evidence to suggest that depression is underdiagnosed in minority ethnic groups, including African Americans and Hispanics (Shao et al, 2016). Lack of reimbursement incentives or missing information in patient’s medical records may also play an important role in healthcare providers’ screening behavior [21]. Underdiagnoses may lead to a low estimated prevalence of depression in certain groups. Low screening rates translate into missed opportunities for treatment. More research is therefore needed to understand the determinants of undiagnosed cases for both diabetes and depression to avoid algorithmic bias.

Given the fragmentation of care, limited funding, high costs of co-occurrence to the individual and the healthcare system, and that the reported co-occurrence is relatively small, having a clinical instrument for rapid and efficient diagnoses, that will help us deploy scarce resources to those most in need seems paramount. Leading questions about lifestyle choices, educational attainment and family life are simple and likely effective strategies to screen patients with diabetes and at risk for depression. Asking targeted questions might also have spillover benefits beyond the diagnosis of co-occurrence by increasing patients’ satisfaction.

At the state level, quality improvement initiatives may be more successful if targeted to areas where the prevalence of both diabetes and mental health issues are high and where there are limited resources dedicated to mental health services.

15 Jan 2020

PONE-D-19-22449

Co-occurrence of diabetes and depression in the U.S.: How can we target those at risk better?

PLOS ONE

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Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Abebaw Fekadu, MD/PhD

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed:

Bogner, Hillary R., and Heather F. de Vries McClintock. "Costs of coexisting depression and diabetes." (2016): 594-595.

In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

3. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

4. Thank you for stating the following in the Financial Disclosure section:"The author received no specific funding for this work."

Thank you for stating the following in the Competing Interests section:"The author declares that no competing interests exist."

We note that one or more of the authors are employed by a commercial company: "IMPAQ International,"

a)Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

b). Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to  PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and  there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Additional Editor Comments:

Title:

I suggest modifying title. The “risk” factors described are weakly indicative of “co-occurrence” and recommendations provided are not robust enough to include 'risk' within the title.

Abstract:

Please provide full name for BRFSS

Background:

Good background overall.

Can make more a bit more detail in the background on existing studies of state level variation in the distribution of the conditions of interest and what the current report may add.

Design: The description in this section is one of statistical analytic approach rather than design. This needs rewriting.

Results:

Correlation of 0.17 is very low despite statistical significance. This needs to be reflected upon.

All “exhibit” should be relabelled as Figure.

Table: Would be helpful for readers to be explicit about what the coefficients are representing.

In the text description of “Exhibit 3” (page 7, line 21-22), the detailed reference to the axis should go as an explanatory note under the exhibit itself.

Discussion:

Does not pay attention in any depth to the state level differences and why comorbidity at the state level may be explained.

It would have also been good to have concluding remarks or reflections at the end.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I would like more information about why the authors used the data analysis they chose. I also feel that the one scatter plot on individuals could be re-done . it is impossible to decipher. I also feel the conclusion is weak and could be re-done to highlight why the article is important

Reviewer #2: The manuscript entitled 'Co-occurrence of diabetes and depression in the U.S.: How can we target those at risk better?' with the aim to evaluate demographic and socio-economic indicators associated with both depression and diabetes at the macro (across-states) and micro level (individuals)'

This is quite an interesting study. However, the manuscript requires improvement based on the following comments.

Abstracts

Page 2 Line 12, BMI >30 or BMI≥30? Likewise in discussion section i.e. Page 8 Line 17, 19, Page 9 Line 1.

Try to avoid using active sentence i.e 'I' (Page 5 Line 7,22; Page 6 Line 10)

Page 5, more information/description to be provided on BRFSS such as data type, type of measurement/ test/ inventories/ questionnaires etc i.e measure for diabetes, depression etc

Page 5 Line 10-13, more information to be provided.

Subjects

Page 5 Line 16-23, this section requires revision to state that these subjects (depression and/or diabetes) were identified in BRFSS via the two questions which were found in BRFSS.

Page 5 Line 16-17, to clearly state how many subjects were analyzed and to state if any missing data occurred in the database that was used in the analysis.

Design

Page 6 Line 6, 7, the words 'could be', 'might be' to be avoided.

Page 6 Line 13, 14, 16, 17, the equation to be labelled.

More information to be provided on the statistical analysis such as type of regression analysis, model fit, level of acceptance significance, 1 or 2 tailed test etc and description on how the analyses were performed on state and individual level. The statistical software STATA including the publisher name and version to be stated.

Results

Page 7 Line 2, 17% to be replaced with 17.2%.

The word Exhibit to be replaced with word Table.

Exhibit 1, technically p value cannot be written as 0.000 (to use symbol < to denote less). Denote what 'omitted' and 'ρ' refers to in the table footnote. CIL and CIU to be written as 95% CI (Lower, Upper). P>[t] to be written as P. Symbol >= to be replaced with symbol ≥. The category for BMI and veteran to be stated in the table footnote.

Likewise with Exhibit 2. dy/dx to be denoted in table footnote.

Page 7 Line 22 - Page 8 Line 11, total states added up to 51. On Page 5 Line 16, it was stated 50.

Page 9 Line 10 and 12, CI to be written as 95%CI.

Overall, the writeup requires revision in terms of flow, clarity and more information to be provided.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

17 Feb 2020

Editor’s comments

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

http://www.journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and http://www.journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Answer. Thank you. The title page now follows PLOS ONE style template. My affiliation has also been updated. In the main text – all headings follow the names used in the guideline, i.e., I now call “introduction” what was previously defined as “background”.

2. We noticed you have some minor occurrence of overlapping text with the following previous publication(s), which needs to be addressed:

Bogner, Hillary R., and Heather F. de Vries McClintock. "Costs of coexisting depression and diabetes." (2016): 594-595. In your revision ensure you cite all your sources (including your own works), and quote or rephrase any duplicated text outside the methods section. Further consideration is dependent on these concerns being addressed.

Answer. Any overlapping text is accidental. I used here the software that I use to evaluate plagiarism in my students’ essays. The only parallel I found is regarding this section: “The global burden of disease study … prevalence” I have deleted that sentence because it does not change in any meaningful way the arguments in the text and I have added instead other evidence from the literature to highlight the importance of the paper.

If you have specific text suggestions that have not been captured by software, please kindly point those to me as I am unable to identify them.

3. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide.

Answer. The dataset I have used in my analysis is publicly available. Reference 16 provides the links to access the data.

4. Thank you for stating the following in the Financial Disclosure section:"The author received no specific funding for this work."

Thank you for stating the following in the Competing Interests section:"The author declares that no competing interests exist."

We note that one or more of the authors are employed by a commercial company: "IMPAQ International,"

Answer. My former employer, IMPAQ international is not a commercial company but a (for profit) research organization. I was a senior economist there, and all researchers of that institution as well as similar ones have to undergo background checks that guarantee they do not have conflicts of interest. More information about my former employer is available here: https://www.impaqint.com/. While the paper was under review I have become a professor at Georgetown University.

a)Please provide an amended Funding Statement declaring this commercial affiliation, as well as a statement regarding the Role of Funders in your study. If the funding organization did not play a role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript and only provided financial support in the form of authors' salaries and/or research materials, please review your statements relating to the author contributions, and ensure you have specifically and accurately indicated the role(s) that these authors had in your study. You can update author roles in the Author Contributions section of the online submission form.

Answer. All parts of the study (the study design, data collection and analysis, decision to publish, or preparation of the manuscript) were done on my own time and not during IMPAQ’s working hours. Therefore, IMPAQ was not the “funding organization” as my salary then reflected the hours spent on contract work and did not cover projects done in my own time. Also please note my change in affiliation to a university institution.

Please also include the following statement within your amended Funding Statement.

“The funder provided support in the form of salaries for authors [insert relevant initials], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.”

Answer. Both at IMPAQ (previously) and at Georgetown University (not) I (dis not) do not get a salary to write papers. I write papers on my own time and interest-- I have no conflict of interests with any organization that might draw any benefit from my independent research. I am happy to state the truth and to explain further my circumstances, with the amount of detail the editors suggest would be appropriate. However, the suggested statement does not reflect my situation.

If your commercial affiliation did play a role in your study, please state and explain this role within your updated Funding Statement.

Answer. As aforementioned, my former employer is not a commercial company but an interdependent research organization whereby employees have to be able to demonstrate no COIs. My current employer is a University, with the same requirements.

b). Please also provide an updated Competing Interests Statement declaring this commercial affiliation along with any other relevant declarations relating to employment, consultancy, patents, products in development, or marketed products, etc.

Answer. I do not have now (or ever had) a commercial affiliation. I hope my previous answers clarify any confusion related to my former employer.

Within your Competing Interests Statement, please confirm that this commercial affiliation does not alter your adherence to all PLOS ONE policies on sharing data and materials by including the following statement: "This does not alter our adherence to PLOS ONE policies on sharing data and materials.” (as detailed online in our guide for authors http://journals.plos.org/plosone/s/competing-interests) . If this adherence statement is not accurate and there are restrictions on sharing of data and/or materials, please state these. Please note that we cannot proceed with consideration of your article until this information has been declared.

Answer. The data I used (BRFSS) in my analysis is publicly available. I have included a link in the paper (reference number 16) to where readers can download the data.

Please include both an updated Funding Statement and Competing Interests Statement in your cover letter. We will change the online submission form on your behalf.

Additional Editor Comments:

Title: I suggest modifying title. The “risk” factors described are weakly indicative of “co-occurrence” and recommendations provided are not robust enough to include 'risk' within the title are not robust enough to include 'risk' within the title.

Answer. As requested, I deleted from the title “How can we target those at risk better?”

Abstract: Please provide full name for BRFSS

Answer. Done. The text in the abstract now reads “Behavioral Risk Factor Surveillance System”

Background: Good background overall. Can make more a bit more detail in the background on existing studies of state level variation in the distribution of the conditions of interest and what the current report may add.

Answer. Thank you. More details in the background has been added on studies that state national level variation. There are no peer reviewed studies looking at differences across states.

Design: The description in this section is one of statistical analytic approach rather than design. This needs rewriting.

Answer. Following the editor’s advice, I now use the term “statistical approach” rather than design.

Results: Correlation of 0.17 is very low despite statistical significance. This needs to be reflected upon. All “exhibit” should be relabelled as Figure. Table: Would be helpful for readers to be explicit about what the coefficients are representing.

Answer. Thank you. All exhibits have now been re-labeled as figures and tables.

In the text description of “Exhibit 3” (page 7, line 21-22), the detailed reference to the axis should go as an explanatory note under the exhibit itself.

Answer. Thank you. A note has now been added under Figure 1, as requested.

Discussion: Does not pay attention in any depth to the state level differences and why comorbidity at the state level may be explained. It would have also been good to have concluding remarks or reflections at the end.

Answer. Thank you. In the results and discussion, differences across states are explained in depth – however, understanding why these differences come about are out of the scope of this paper, as the data available does not allow us to answer that question.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I would like more information about why the authors used the data analysis they chose. I also feel that the one scatter plot on individuals could be re-done . it is impossible to decipher. I also feel the conclusion is weak and could be re-done to highlight why the article is important

Answer. Reviewer #1 does not provide any guidance on how s/he would like Figure 2 to be changed or why it is hard for s/he to understand it. I added footnoted to assist the reader with interpretation.

Reviewer #2: The manuscript entitled 'Co-occurrence of diabetes and depression in the U.S.: How can we target those at risk better?' with the aim to evaluate demographic and socio-economic indicators associated with both depression and diabetes at the macro (across-states) and micro level (individuals)'

This is quite an interesting study. However, the manuscript requires improvement based on the following comments.

Abstracts Page 2 Line 12, BMI >30 or BMI BMI≥3030? Likewise in discussion section i.e. Page 8 Line 17, 19, Page 9 Line 1.

Answer. Thank you. Obesity was denoted with BMI≥30. This has been amended in the text and made consistent with the computations.

Try to avoid using active sentence i.e 'I' (Page 5 Line 7,22; Page 6 Line 10)

Answer. All active sentences were avoided. On page 5, I now use “This analysis uses”. On page 6, line 2, I changed the previously active voice into passive voice: “ Women….were excluded from the sample”. On page 6, line 13, I now write “All analyses control for…”

Page 5, more information/description to be provided on BRFSS such as data type, type of measurement/ test/ inventories/ questionnaires etc

i.e measure for diabetes, depression etc

Page 5 Line 10-13, more information to be provided.

Answer. I added clarification on the type of data available in BRFSS as requested

Subjects Page 5 Line 16-23, this section requires revision to state that these subjects (depression and/or diabetes) were identified in BRFSS via the two questions which were found in BRFSS.

Answer. The fact that the questions are found in BRFSS is now stated in the text as requested

Page 5 Line 16-17, to clearly state how many subjects were analyzed and to state if any missing data occurred in the database that was used in the analysis.

Answer. The information requested is stated on page 5, line 21. 477,665 respondents answered the question about diabetes and depression. 40,921.00 (or 9% of the sample) did not have complete information on all the demographic characteristics the study included and therefore were excluded from the sample. I re-edited the section to make the exposition clearer.

Design Page 6 Line 6, 7, the words 'could be', 'might be' to be avoided.

Answer. “Could be” and “might be” gave been substituted with “would”

Page 6 Line 13, 14, 16, 17, the equation to be labelled.

Answer. The equations (previously in line 13 and 14) were label as the reviewer suggested. The correlation matrix was not labelled because that is NOT an equation not it representants two different arguments like the reviewer seems to indicate. It is one single argument.

More information to be provided on the statistical analysis such as type of regression analysis, model fit, level of acceptance significance, 1 or 2 tailed test etc and description on how the analyses were performed on state and individual level. The statistical software STATA including the publisher name and version to be stated.

Answer. On page 5, it was already stated that the statistical software used is STATA 15. It is also stated that I use a bivariate normal model. Standard errors, P-values and confidence intervals are presented in Tables 1 and 2. The study is descriptive and does not perform differences across cohorts therefore the comment being made on tests is not relevant in this case.

Results Page 7 Line 2, 17% to be replaced with 17.2%.

Answer. The change requested was done.

The word Exhibit to be replaced with word Table.

Answer. The change requested was done.

Exhibit 1, technically p value cannot be written as 0.000 (to use symbol < to denote less). Denote what 'omitted' and 'ρ' refers to in the table footnote. CIL and CIU to be written as 95% CI (Lower, Upper). P>[t] to be written as P. Symbol >= to be replaced with symbol ≥. The category for BMI and veteran to be stated in the table footnote. Likewise with Exhibit 2. dy/dx to be denoted in table footnote.

Answer. 0.000 was replaced through with <0.0001 as requested. A footnote has been added under table 1, as requested. 95% CI (Lower, Upper) has been written in lieu of CIL CIU. The Symbol >= has been replace.

A footnote for dy/dx has been added in the footnote under Exhibit 2 – now labelled as Table 2. Note however that the table already explains that dy/dx means: “marginal probability”.

Page 7 Line 22 - Page 8 Line 11, total states added up to 51. On Page 5 Line 16, it was stated 50.

Answer. It is 50 states plus DC. The counts add up to 51 and are consistent with the description.

Page 9 Line 10 and 12, CI to be written as 95%CI.

Answer. The change requested was added.

Submitted filename: Response to reviewers.docx

Click here for additional data file.

18 May 2020

PONE-D-19-22449R1

Co-occurrence of diabetes and depression in the U.S.

PLOS ONE

Dear Dr. Alva,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

Please make minor revisions following the reviewers' comments.

==============================

We would appreciate receiving your revised manuscript by Jul 02 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

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An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Nayu Ikeda, Ph.D.

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: (No Response)

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I was concerned on the previous review that the graphs were not explained in any way. The author has added text below the graphs to explain what they show. I would remind the author to make the manuscript reader friendly, as everyone who reads this may not be in the field of the writer.

Reviewer #2: Minor comments

Table 1, for Did not graduate from high school, the figures 33.9% 41.7% , 16.8% 23.9% to be aligned parallel with other figures.

Table 2, Diabetes only P(y1=1, y2=0) (Non hispanic black, hispanic, native, others) typo <.0001<.0001.

The word marginal probability in the first column to be replaced with variables.

STATA which was stated for 'The survey design was specified as follows, using Stata 15: svyset [pweight=_LLCPWT], strata(_STSTR) psu(_PSU) to be stated in statistical analysis section with citation 'STATA: Release 15. College Station, TX: StataCorp LLC' and the level of accepted statistical significant.

Some references did not conform to the journal format.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

18 May 2020

Thank you for the comments. These have been fully addressed both the marked and unmarked copies uploaded.

My response to each suggestion raised is reported below:

• Table 1, for Did not graduate from high school, the figures 33.9% 41.7% , 16.8% 23.9% to be aligned parallel with other figures.

Answer: Thank you. The table was re-formatted

• Table 2, Diabetes only P(y1=1, y2=0) (Non hispanic black, hispanic, native, others) typo <.0001<.0001.

Answer: Thank you. The typo has now been corrected.

• The word marginal probability in the first column to be replaced with variables.

Answer: Thank you. This was changed.

• STATA which was stated for 'The survey design was specified as follows, using Stata 15: svyset [pweight=_LLCPWT], strata(_STSTR) psu(_PSU) to be stated in statistical analysis section with citation 'STATA: Release 15. College Station, TX: StataCorp LLC' and the level of accepted statistical significant.

Answer: The STATA citation has been added and all the references have been reformatted.

• Some references did not conform to the journal format.

Answer: All references have now been formatted using the ICMJE style

Submitted filename: response to referees.docx

Click here for additional data file.

2 Jun 2020

Co-occurrence of diabetes and depression in the U.S.

PONE-D-19-22449R2

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10 Jun 2020

PONE-D-19-22449R2

Co-occurrence of diabetes and depression in the U.S.

Dear Dr. Alva:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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on behalf of

Dr. Nayu Ikeda

Academic Editor

PLOS ONE