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. 2020 Oct 8;15(10):e0240394. doi: 10.1371/journal.pone.0240394

Non-communicable diseases and inequalities increase risk of death among COVID-19 patients in Mexico

Juan Pablo Gutierrez 1,*, Stefano M Bertozzi 2,3,4
Editor: Brecht Devleesschauwer5
PMCID: PMC7544063  PMID: 33031467

Abstract

Background

The SARS-CoV-2 pandemic compounds Mexico’s pre-existing challenges: very high levels of both non-communicable diseases (NCD) and social inequity.

Methods and findings

Using data from national reporting of SARS-CoV-2 tested individuals, we estimated odds of hospitalization, intubation, and death based on pre-existing non-communicable diseases and socioeconomic indicators. We found that obesity, diabetes, and hypertension are positively associated with the three outcomes in a synergistic manner. The municipal poverty level is also positively associated with hospitalization and death.

Conclusions

Mexico’s response to COVID-19 is complicated by a synergistic double challenge: raging NCDs and extreme social inequity. The response to the current pandemic must take both into account both to be effective and to ensure that the burden of COVID-19 not falls disproportionately on those who are already disadvantaged.

Introduction

The current SARS-CoV-2 pandemic has already infected more than 14 million individuals and caused over 600 thousand deaths worldwide [1]. There is an urgent need to better understand who is at higher risk of worse outcomes, including death. A recent study suggests that 22% of the global population is at risk of developing severe COVID-19 based on underlying conditions, principally non-communicable diseases (NCD); 4% of global population (included in the 22%) is at high-risk based on the same conditions [2]. Poverty is associated with increased prevalence of NCDs (and with obesity, another risk factor not considered in the Clark et al study) but it also is associated with increased risk of infection, poorer control of NCDs and poorer access to quality health services, thus compounding the impact on the poor [3].

Previous studies have reported that obesity and diabetes are associated with greater COVID-19 disease severity [4–6], and that has also been documented in Mexico using data from the initial phase of the pandemic [7]. Other conditions that have been positively associated with the severity of COVID-19 –and that are also major contributors to the global burden of disease—[8] include: smoking, chronic cardiac disease, chronic pulmonary disease and chronic kidney disease [9, 10]. An analysis of COVID-19 cases in the United Kingdom found that male sex, older age, economic deprivation, uncontrolled diabetes, and asthma increased risk of death [11].

The reported prevalence of obesity in Mexico among individuals 15 years of age and older is 32.4%, the second highest in the OECD, behind only the USA. [12]. Data from the Mexican National Health and Nutrition Surveys describe the growth in prevalence of obesity since at least 2000; the growth is faster among younger individuals [13].

Diabetes is the single largest contributor to disease burden in Mexico, with a prevalence of about 15% of the adult population. Hypertension is also very important, with a prevalence of 30% [14–16].

Previous analyses have highlighted major inequalities in health in Mexico, as individuals with lower incomes face a higher probability of complications once diagnosed with a non-communicable disease (NCD) and an increased financial burden that affects their ability to adhere to treatment recommendations [17, 18]. Given COVID-19’s association with NCDs, the fact that NCDs are both more prevalent and less well controlled among the poor, and the fact that the poor are at greater risk of infection because they are unable to shelter in place without income—the pandemic is expected to further worsen the health of the poor compared to those who are better off, increasing health inequity [3, 19–21].

In Mexico, the triad of NCDs, inequity and COVID doesn’t occur in the context of a high-functioning health care system, but rather one that is characterized by fragmentation, under-funding, lax regulatory oversight and a high proportion of poorly-trained health personnel. The unfortunate result is low average health care quality which translates into unacceptably high mortality rates for severe COVID-19 disease.

Using data from SARS-CoV-2-positive individuals in Mexico, our aim is to assess NCDs and socioeconomic status as risk factors for COVID-19 severe outcomes, in particular hospitalization, intubation and death.

Methods

We analyzed public data reporting all individuals tested for SARS-CoV-2 in Mexico compiled and published by the Directorate General of Epidemiology (Ministry of Health). It is updated daily and included basic demographics and comorbidities, in addition to the test result [22]. The reports also include place of residence and whether the individual died. We only included adults age 20 and older in our analyses as our interest is on whether NCDs and inequalities are related to worse outcomes among patients with COVID-19. The prevalence of NCDs and the incidence of severe COVID-19 disease are both very low among those under 20.

Testing for SARS-CoV-2 in Mexico at public facilities is only performed if the individual has clinical symptoms suggestive of COVID-19. Thus, not all individuals who ask for a test are tested, but individuals who were tested were included in the dataset, albeit with variable reporting delays. It is important to note that included cases represent both ambulatory and hospitalized individuals.

The dataset is comprised of cases from the national surveillance system in which 475 clinical facilities in the country test at least 10% of all cases with mild respiratory symptoms and 100% of cases with severe symptoms. Also included are cases with severe symptoms reported from all clinical facilities not included in the national surveillance system [23].

The dataset includes 35 variables: five variables related to identification of the case (date of the report, unique case identifier, whether the testing facility was a surveillance site or not, provider, state where the facility is located); ten variables capture the demographic characteristics of the individual (sex, age, state of birth, state and municipality [county] of residence, nationality, migration status, and, for imported cases, country of origin and native language); for woman there is a variable on current pregnancy; ten variables report pre-existing conditions obtained by individual self-report (diabetes, COPD, asthma, immunosuppression, hypertension, cardiovascular disease, obesity, chronic renal disease, smoking, and “other conditions”); two variable capture SARS-CoV-2 status (result of the test and if the test subject reported contact with another case); and six variables related to COVID-19 case management (reported date of first symptoms, if care was ambulatory or hospitalized, date of hospitalization—for those hospitalized, date of death, intubation, diagnosis of pneumonia, and if the patient required critical care [ICU]).

Underlying conditions reported in the dataset are reported by facilities in the country using their standard reporting procedures and did not include further detail on those conditions (such as severity or whether the diagnosis was confirmed).

For the analyses we used the variables as reported. We excluded in the regression analysis observations with missing values as those were a negligible fraction of the database, at less than 0.5% of observations. Overall, for the regression analysis, 3.5% of observations were excluded (23,037) due to missing values. The incidence of complications (hospitalization, intubation, death) among the excluded observations were similar to the included observations.

Descriptive statistics were produced for all tested individuals who have a result and then separately, comparing those who tested positive and negative for SARS-CoV-2. In order to take into account local epidemic dynamics, standard errors for the descriptive statistics were produced taking into account clustering at the municipality (county) level.

We estimated a mixed effects multivariate logistic regression to estimate the probabilities of being hospitalized, intubated (for those hospitalized) and of dying as a function of sex, age, comorbidities, health provider, whether the individual speaks an indigenous language, and quintiles of the proportion of the population living in poverty in the municipality (county) of residence. The model included municipality random effects.

Both descriptive analyses and regressions were conducted using Stata 15.0 (Stata Corp). As the model includes multiple comparisons, we estimated the false discovery rate using the Benjamini–Hochberg procedure by comparing each p-value to its Benjamini-Hochberg critical value, (i/m)Q, using 5% as Q [24].

For the percentage of the population living below the poverty line, we used the official multidimensional measure in Mexico that includes income and six measures of social deprivation (education, health, social security, housing, housing utilities, and food security) produced by Mexico´s National Council on Evaluation of Social Policy [25] which classifies municipalities by the proportion of individuals living in poverty.

Results

Characteristics of the 1,378,002 individuals who were tested for SARS-CoV-2 in Mexico through September 16, 2020 are reported in Table 1, comparing those with positive and negative results (n = 654,858 and 723,144, respectively). From the pool of individuals tested, those who tested positive for SARS-CoV-2 were more likely to be male (52.21% vs. 46.13%, p < 0.001) and older (46.017 years vs. 42.33 years, p < 0.001). While the percentage of individuals who speak an indigenous language was low (0.88%), a higher proportion were SARS-COV-2 positive (1.01% vs 0.76%, p < 0.01). The proportion of individuals living in poverty at the municipality (county) level was similar between those who tested positive and negative.

Table 1. Sociodemographic and health conditions (% and 95% confidence interval) of Mexicans tested for SARS-CoV-2, total and by test result.

Sociodemographic characteristics All tested SARS-CoV-2 (+) SARS-CoV-2 (-) p value
Sex (Male) 49.02 52.21 46.13 <0.001
(48.60–49.44) (51.80–52.63) (45.59–46.67)
Age (Average) 44.11 46.07 42.33 <0.001
(43.90–44.32) (45.84–46.30) (42.13–42.52)
Indigenous 0.88 1.01 0.76 <0.001
(0.73–1.04) (0.84–1.19) (0.62–0.90)
Ministry of Health 65.13 58.36 71.25 <0.001
(62.63–67.62) (55.89–60.83) (68.64–73.87)
Social security 31.91 38.50 25.94 <0.001
(29.50–34.32) (36.06–40.94) (23.47–28.41)
Private 2.96 3.14 2.81 0.297
(1.90–4.03) (2.27–4.01) (1.52–4.09)
Municipality poverty Q1 13.14 12.39 13.83 0.115
(6.78–19.51) (6.36–18.42) (7.06–20.60)
Q2 54.46 54.04 54.84 0.573
(45.74–63.18) (45.72–62.35) (45.57–64.11)
Q3 25.04 26.05 24.12 0.074
(17.92–32.16) (19.10–33.00) (16.71–31.53)
Q4 6.61 6.66 6.56 0.756
(5.00–8.21) (5.12–8.20) (4.84–8.27)
Q5 0.75 0.87 0.65 <0.001
(0.55–0.96) (0.63–1.10) (0.47–0.84)
Health conditions All tested SARS-CoV-2 (+) SARS-CoV-2 (-) p value
Obesity 16.39 18.77 14.24 <0.001
(15.84–16.95) (18.23–19.32) (13.68–14.80)
Diabetes 12.99 16.19 10.10 <0.001
(12.57–13.42) (15.76–16.62) (9.73–10.46)
Hypertension 17.13 20.26 14.30 <0.001
(16.53–17.74) (19.62–20.91) (13.80–14.80)
Asthma 2.86 2.58 3.11 <0.001
(2.69–3.03) (2.42–2.75) (2.91–3.30)
COPD 1.41 1.53 1.29 0.009
(1.32–1.49) (1.44–1.62) (1.20–1.38)
Chronic renal disease 1.84 1.96 1.72 <0.001
(1.73–1.94) (1.86–2.06) (1.59–1.85)
Cardiovascular disease 1.99 2.04 1.95 0.022
(1.90–2.08) (1.95–2.14) (1.84–2.05)
Smoking 7.47 9.68 8.63 <0.001
(6.97–7.97) (8.84–10.52) (7.95–9.31)
COVID-19 Outcomes All tested SARS-CoV-2 (+) SARS-CoV-2 (-) p value
Hospitalized 17.38 25.06 10.42 <0.001
(16.18–18.58) (23.65–26.47) (9.53–11.31)
Intubated (of those hospitalized) 15.53 18.14 9.84 <0.001
(14.67–16.39) (17.16–19.12) (9.19–10.49)
Death 6.45 10.95 2.37 <0.001
(5.96–6.93) (10.32–11.58) (2.13–2.61)
Obs 1,378,002 654,858 723,144
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In terms of health provider, 65.13% of individuals were treated at Ministry of Health (MoH) facilities, 31.91% at social security facilities and 2.96% by private providers. Among those who tested positive, 58.36% received care from the MoH, 38.50% from social security and 3.14% from private providers.

Individuals who tested positive to SARS-CoV-2 were more likely to have a diagnosis of obesity (18.77% vs 14.24%, p < 0.001), diabetes (16.19% vs 10.10%, p < 0.001), hypertension (20.26% vs 14.30%, p < 0.001), COPD (1.53% vs 1.29%) and diagnosis of chronic renal disease (1.96% vs 1.72%, p < 0.001) compared to those who tested negative, and were more likely to report smoking (9.68% vs 8.63%, p < 0.001). In contrast, they were less likely to report asthma (2.58% vs 3.11%, p < 0.001). There was no significant difference by diagnosis of cardiovascular disease (2.04% vs 1.95%, p = 0.071).

Hospitalization

Being male was associated with an increased probability of being hospitalized (OR 1.66). Probability also increased with age, with an OR of 1.53 for those 30 to 39 years compared to 20 to 29 years, 2.99 for 40 to 49 years, 5.64 for 50 to 59 years, 10.95 for 60 to 69 years, 17.40 for 70 to 79 years, and 22.55 for those 80 years and older (Table 2).

Table 2. Odds (95% confidence interval) of hospitalization, intubation and death for individuals with COVID-19 based on comorbidities, demographics and socioeconomic indicators†.

% (1) (2) (3)
Hospitalization Intubation Death
Sex (Male = 1) 49.02 1.66*** 1.26*** 1.77***
(48.60–49.44) (1.63–1.68) (1.23–1.30) (1.74–1.81)
Age 20 to 29 15.83 1.00 1.00 1.00
(15.42–16.26)
Age 30 to 39 23.13 1.53*** 1.30*** 2.11***
22.85–23.42) (1.48–1.58) (1.17–1.44) (1.95–2.28)
Age 40 to 49 22.37 2.99*** 1.73*** 5.52***
(22.14–22.60) (2.90–3.08) (1.57–1.91) (5.13–5.94)
Age 50 to 59 18.38 5.64*** 2.21*** 12.27***
(18.12–18.65) (5.47–5.82) (2.00–2.43) (11.42–13.18)
Age 60 to 69 11.53 10.95*** 2.71*** 26.06***
(11.29–11.78) (10.59–11.32) (2.46–2.98) (24.25–28.00)
Age 70 to 79 6.06 17.40*** 2.77*** 43.48***
(5.86–6.27) (16.76–18.07) (2.51–3.06) (40.40–46.79)
Age 80+ 2.69 22.55*** 2.20*** 60.53***
(2.57–2.81) (21.51–23.64) (1.98–2.44) (56.02–65.42)
Indigenous Speaker = 1 1.01 1.64*** 0.89* 1.43***
(0.84–1.19) (1.52–1.76) (0.79–1.01) (1.32–1.55)
Neither obese, diabetic nor hypertensive 61.33 1.00 1.00 1.00
(60.48–62.18) (1.00–1.00) (1.00–1.00) (1.00–1.00)
Neither obese nor diabetic. Hypertensive 8.10 1.42*** 1.16*** 1.39***
(7.85–8.35) (1.39–1.46) (1.11–1.21) (1.35–1.44)
Neither obese nor hypertensive. Diabetic 5.71 2.28*** 1.10*** 1.82***
(5.56–5.87) (2.22–2.34) (1.05–1.15) (1.76–1.88)
Not obese. Diabetic & hypertensive 5.98 2.36*** 1.09*** 1.93***
(5.74–6.21) (2.30–2.43) (1.05–1.14) (1.87–1.99)
Obese; neither diabetic nor hypertensive 10.91 1.62*** 1.30*** 1.68***
(10.57–11.25) (1.59–1.66) (1.23–1.36) (1.62–1.73)
Obese & hypertensive. Not diabetic 3.35 1.86*** 1.40*** 1.95***
(3.21–3.48) (1.80–1.93) (1.31–1.48) (1.87–2.03)
Obese & diabetic. Not hypertensive 1.68 2.85*** 1.32*** 2.44***
(1.62–1.74) (2.72–2.98) (1.22–1.43) (2.30–2.58)
Obese, diabetic & hypertensive 2.76 2.82*** 1.34*** 2.48***
(2.63–2.88) (2.71–2.92) (1.27–1.42) (2.38–2.59)
Neither asthma, COPD, nor smoker 89.90 1.00 1.00 1.00
(88.54–89.47) (1.00–1.00) (1.00–1.00) (1.00–1.00)
Smoking. Neither asthma nor COPD 6.97 0.95*** 1.03 0.97
(6.49–7.44) (0.92–0.98) (0.98–1.08) (0.94–1.01)
COPD. Neither asthma nor smoker 1.14 1.42*** 1.07* 1.35***
(1.08–1.21) (1.34–1.50) (0.99–1.16) (1.27–1.42)
Smoking & COPD. Not asthma 0.27 1.43*** 0.96 1.12**
(0.24–0.30) (1.27–1.61) (0.83–1.11) (1.01–1.26)
Asthma. Neither COPD nor smoker 2.28 0.94*** 1.01 0.90***
(2.12–2.44) (0.89–0.98) (0.92–1.12) (0.84–0.96)
Asthma & smoking. Not COPD 0.18 1.03 0.86 0.96
(0.16–0.19) (0.86–1.22) (0.60–1.23) (0.75–1.24)
Asthma & COPD. Not smoking 0.08 0.97 0.98 1.07
(0.07–0.09) (0.79–1.20) (0.71–1.35) (0.86–1.34)
Asthma, COPD & smoking 0.03 0.48*** 0.70 0.44***
(0.03–0.04) (0.34–0.68) (0.38–1.29) (0.29–0.68)
Chronic renal disease 1.96 2.73*** 1.04 2.40***
(1.86–2.06) (2.60–2.86) (0.98–1.10) (2.30–2.51)
Cardiovascular disease 2.04 1.15*** 0.98 1.06***
(1.95–2.14) (1.10–1.20) (0.92–1.05) (1.02–1.12)
Ministry of Health 58.36 1.00 1.00 1.00
(55.89–60.83)
Social security 38.50 3.13*** 1.29*** 2.91***
(36.06–40.94) (3.08–3.18) (1.25–1.33) (2.86–2.97)
Private health provider 3.14 2.81*** 1.06 0.85***
(2.27–4.01) (2.70–2.93) (0.97–1.16) (0.79–0.92)
Quintiles of share of poverty
Quintile 1 (0% to 19% of individuals are poor) 12.39 1.0 1.0 1.0
(6.36–18.42)
Quintile 2 (20% to 39% of individuals are poor) 54.04 1.36** 0.97 1.15
(45.72–62.35) (1.01–1.84) (0.79–1.20) (0.94–1.41)
Quintile 3 (40% to 59% of individuals are poor) 26.05 1.97*** 0.84 1.39***
(19.10–33.00) (1.48–2.62) (0.69–1.04) (1.14–1.69)
Quintile 4 (60% to 79% of individuals are poor) 6.66 2.55*** 0.79** 1.60***
(5.12–8.20) (1.92–3.39) (0.64–0.97) (1.32–1.95)
Quintile 5 (80% & + of individuals are poor) 0.87 3.25*** 0.98 1.95***
(0.63–1.10) (2.40–4.41) (0.76–1.25) (1.56–2.43)
var(_cons[Municipality]) 2.39*** 1.29*** 1.33***
(2.21–2.59) (1.24–1.35) (1.28–1.37)
Observations 631,821 158,938 631,821
Number of groups 2,173 1,963 2,173
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†Multiple comparisons controlled using a false discovery rate approach, following the Benjamini–Hochberg procedure comparing each p-value with its Benjamini-Hochberg critical value, (i/m)Q, using 5% as Q. All variables with a p value < 0.05 have also values < the Benjamini-Hochberg critical value.

*** p<0.01,

** p<0.05,

* p<0.1

The odds of hospitalization were 2.82 for those obese, diabetic, and hypertensive compared to those with none of those conditions, a higher probability that for any of those conditions alone. Those with COPD were more likely to being hospitalized (OR 1.42), while smoking alone was negatively associated with hospitalization (OR 0.95). Chronic renal disease was associated with a higher probability of hospitalization (OR 2.73) as well as cardiovascular disease (OR 1.15).

Those cared for by social security and private providers were more likely to be hospitalized (OR 3.13 & 2.81, respectively) compared to those from MoH facilities. Individuals who speak an indigenous language were more likely to be hospitalized (OR 1.64) compared to those who don´t. In terms of share of poverty at municipality level, the odds of hospitalization increase with the share of poverty which goes from OR 1.36 for individuals from municipalities with a share of poverty between 20% to 39% up to OR 3.25 for those from municipalities with a share of poverty of 80% and higher.

Intubation

Being male was associated with an increased probability of being intubated (OR 1.26) as was age, with the probability increasing monotonically to age 70 to 79 (OR 2.77) (Table 2). It is lower for those 80 and above, but not significantly, despite an increase in mortality. That pattern is consistent with a reluctance to intubate patients over 80 at comparable levels of severity.

Being obese, diabetic and hypertensive increased the odds of intubation both as individual conditions and as comorbidities in a synergistic way: the odds ratio for intubation was 1.34 for those who were obese, diabetic & hypertensive compared to individuals with none of those conditions, higher than for any of these conditions alone.

Neither asthma, COPD nor smoking were related to the odds of intubation among the analyzed individuals. Chronic renal disease was positively associated with intubation (OR 1.15).

Being a patient in one of the social security institutions was associated with a higher probability of intubation (OR 1.29) compared to Ministry of Health (MoH) facilities. The probability of intubation was not different between the MoH and private providers. Compared to those from municipalities with the lowest share of poverty, individuals from municipalities with higher shares of poverty were less likely to be intubated; there was no association between intubation and speaking an indigenous language.

Mortality

The probability of dying from COVID-19 was higher for males (OR 1.77) compared to females and increased with age: odds of death compared to individuals 20 to 29 were 2.11 for those 30 to 39, 5.52 for those 40 to 49, 12.27 for those 50 to 59, 26.06 for those 60 to 69, 43.48 for those 70 to 79 and 60.53 for those 80 and older. Speaking an indigenous language was associated with an increased probability of dying (OR 1.43) (Table 2).

The odds ratio for dying from COVID-19 was 2.48 for individuals with a diagnosis of obesity, diabetes and hypertension compared to those without any of those conditions. Being only obese (neither diabetic nor hypertensive) had an odds ratio of 1.68, for only diabetic it was 1.82, and for only hypertensive, 1.39.

Individuals with COPD were more likely to die from COVID-19 compared to those that were neither smokers, nor COPD, nor asthmatics with an OR of 1.35. Those with asthma but neither COPD nor smoking were less likely to die from COVID-19 with an OR of 0.90. Those with asthma, COPD and smoking had also a lower probability of dying of COVID-19 (OR 0.44). Individuals with chronic renal disease were more likely to die, with an odds ratio of 2.40.

Compared to individuals from Ministry of Health services, those from social security were more likely to die, with an OR of 2.91, while those from private providers were less likely to die, with an OR of 0.85.

Regarding share of poverty at the municipality level, compared to those with the lowest share of poverty, individuals from municipalities with a share of poverty between 40% to 59% had an increased probability of dying with an OR of 1.39, as well as those from municipalities with share of poverty between 60% to 79% and those from municipalities with a share of poverty of 80% and larger, with ORs of 1.60 and 1.95, respectively.

Discussion

Given the high burden of NCDs in Mexico, in particular obesity, diabetes and hypertension, the results presented here represent a huge challenge for the county in terms of the current COVID-19 pandemic. Mexico is behind only the USA among OECD member countries in terms of prevalence of obesity among adults and has the highest rate in the OECD for diabetes [12, 26]. While the reported prevalence of these conditions among individuals tested for SARS-CoV-2 in Mexico is lower than the survey-based observed prevalence among the general population, it is similar to the self-reported prevalence of prior diagnosis of these conditions. In 2018 these were: 22.8% for obesity, 10.3% for diabetes, and 18.4% for hypertension [27].

As has been previously suggested, there is an urgent need to provide more complete data on the comorbidities associated with COVID-19 severity [5], in particular those NCDs that the evidence already suggests increase the probability of severe disease and death. For example, there are likely to be important differences by the severity of the comorbid conditions and/or by the type of treatment the patient is receiving.

In addition to the weighty burden of NCDs, Mexico faces another enormous challenge: social inequity. Wealth distribution in Mexico is one of the worst worldwide; Mexico is tied with Chile for the most unequal income distribution in the OECD [28]. Wealth inequity is the major driver of health inequity [29]. Poverty may be linked to higher risk of dying for COVID-19 not only due to the fact that poverty increases the probability of becoming infected because social distancing is more difficult or because the poor are at higher risk of developing NCDs, but also because individuals living in poverty have less access to public services, including health services, and those health services that provide care to them are on average of lower quality. Low-quality healthcare is an important cause of avoidable deaths in Mexico [30].

Mexicans who speak an indigenous language are five times as likely as those who do not to be living in extreme poverty [31]. Both speaking an indigenous language and living in a municipality (similar to a county in the USA) with a higher percentage of the population living in poverty were associated with increased mortality, in a stepwise fashion. There are many possible explanations (preexisting health status, delays in accessing care, and differential quality of care among the most important). Because the poor are also less likely to access the social security or private health systems, this compounds their mortality risk [31]. Our results are consistent with similar studies reported for other countries: in Brazil, COVID-19 mortality has been reported to be associated with minority ethnic background and region of residence [19]. Similarly, in the UK there is evidence of the pandemic disproportionally affecting minority ethnic communities [32].

Mexico requires, as does the rest of the world, a precision public health approach, one that take advantage of available data to design a response that takes into account population characteristics as determinants of risk.

Lower mortality for those individuals with asthma, those who smoke, and those with asthma, COPD and who smoke may be related to the fact that individuals from these groups may be more likely to seek care earlier and to be tested sooner because they are perceived to be at-risk. However, the results are also consistent with other studies that have reported lower risk among smokers [33, 34]. Further analyses and more complete data would be required to better describe these relationships.

This study has an important limitation. It is a study of COVID-19 patients in Mexico and we are not able to correct for disease severity. Thus, when differences are observed among institutions or groups, the difference could be due to differential thresholds for seeking any health care, for hospitalization or for intubation. For example, the higher mortality in the Ministry of Health hospitals compared to the private sector could imply that the same patient, depending on where they were hospitalized, would have a higher chance of dying in an MoH hospital. However, it could equally well represent that a patient needs to be more severely ill to be hospitalized in a MoH hospital than in the private sector. Thus, one must be careful not to assume causality. That said, the significantly higher morbidity and mortality associated with co-morbid conditions probably underestimates their true effect, to the extent that physicians are likely to have a lower severity threshold for recommending hospitalizing for a patient with comorbidities. Additionally, as the national protocol mandates testing for all individuals with severe respiratory conditions who present to health facilities as well as a proportion of those with mild respiratory conditions, results may underestimate how poverty is associated with COVID-19 prognosis as those living in poverty have less access to health services. In terms of association with NCDs, this testing protocol may also underestimate associations, as those with mild respiratory conditions will have a better prognosis. Our socioeconomic indicator, share of poverty at the municipality level, reflects not the condition of the individual but that of the county where she or he lives.

Mexico’s response to COVID-19 is stymied by a synergistic double challenge: raging NCDs and extreme social inequity. The response to the current pandemic must take both of them into account both to be effective and to ensure that the burden of COVID-19 not fall even more disproportionately on those who are already disadvantaged.

Data Availability

Data is available at https://www.gob.mx/salud/documentos/datos-abiertos-152127.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Brecht Devleesschauwer

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

29 Jun 2020

PONE-D-20-15813

Non-communicable diseases and inequalities increase risk of death among COVID-19 patients in Mexico

PLOS ONE

Dear Dr. Gutierrez,

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.

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Academic Editor

PLOS ONE

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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

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes

Reviewer #4: Yes

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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: No

Reviewer #3: Yes

Reviewer #4: Yes

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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

Reviewer #3: Yes

Reviewer #4: Yes

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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: This is an important study sharing data on the COVID-19 experience in Mexico. The findings highlight the burden of non-communicable disease in the country, and the subsequent implications for the COVID-19 pandemic.

My comments on the manuscript are as follows:

I used the link in the document to source the original data and downloaded the csv file. The column headings are in Spanish, and took a little time to figure out, and I did not immediately understand the key. However, initial look at the data reassured me that the results presented in the text reflect the data.

In general, the manuscript is brief, and so I did have some questions on the methods. It would be important to have a clearer understanding of the data source. Who is included, who might be excluded? Are there any data verification steps? As the data are from an administrative source, it is important the authors share as much information as possible about the reliability of the information. I am not familiar with Mexican administrative datasets.

I am struggling to understand the 'pool' of tested patients adequately. What were the country criteria for testing? Where were tests conducted? Were these limited to hospital settings? This may of course be very relevant to the subsequent indigenous language analyses.

The logistic regression models were simple - exploring risks of hospital admission, intubation or death. The models use outcomes in a binary manner - but my evaluation of the dataset led me to believe dates were included for death, so survival models would have been an option. Was this considered?

In the logistic regression, I would be keen to understand the detail of the models controlling for hospitalisation? Surely it would have been more appropriate to analyse intubation only amongst hospitalised patients? Maybe this was the case - but I was not sure.

Could the authors justify the choice of confounders (were these simply reflective of available data)?

The results tables need clearer labelling. I presumed age was mean +/- SD... but this should be explicitly stated. Similarly, variables like 'obesity' need defining clearly.

The rate of admission in the non-sars-cov2 group struck me as high. Perhaps the authors could add the sensitivity of their testing kits in Mexico?

The second table should ideally present the odds ratios alongside 95% confidence intervals. There is an extraordinary amount of hypothesis testing being done, and I would be more comfortable with fewer p values presented!

Along the same lines, there appear to be some unusual interactions, possibly with effect modification, between asthma, COPD and smoking. My instinct these are confounded by age. I would like to know how the authors explored these associations.

Ideally I would have liked to see the logistic regression data accompanied by absolute numbers, to be able to appreciate absolute v relative risks.

The discussion is brief (I appreciate this is a concise report). However, my sense is that the really novel aspects of the results are the descriptives of the cohort in Mexico in terms of burden of NCD, plus the work around deprivation and indigenous language. The observations around age and comorbidity and their relationship with SARS-CoV-2 outcome are well described now.

There are some minor edits required for typographic errors, but overall the language and style are good.

Reviewer #2: Dear Author,

Thank you for the manuscript, as it possibly adds something to the existing literature, presenting risk factors for hospitalization, intubation and death in the Mexican context. A topic of interest that could be better explored in this manuscript is the effect of socioeconomic inequalities in these outcomes.

I think that this letter could be greatly improved with better background and methods, mainly by considering previously existing articles and by clarifying and improving the methodological approach.

It is noteworthy to mention that a similar (smaller) study was also published, without peer review process, (doi: 10.1101/2020.05.11.20098145), although it did not consider any socioeconomic indicator regarding poverty.

Abstract

The abstract should be re-written. There are a lot of different messages and outcomes; The rationale and conclusions should be more aligned with the main outcomes.

Introduction

According to the main outcomes presented, it seems that the study aims to assess risk factors, including a socioeconomic indicator, for (1) hospitalization, (2) intubation and (3) death. Having said this, the goals should be redefined accordingly.

Several already published studies with similar aims should also be referred in this section. Some examples (doi): 10.1136/bmj.m1985, 10.1080/13685538.2020.1774748, 10.1101/2020.05.06.20092999, 10.1002/jmv.26050 or 10.1016/j.jinf.2020.04.021. Other literature on inequalities and COVID-19 could also be considered.

Methods and Results

The sample considered in this study should be “patients with COVID-19 (confirmed cases with SARS-CoV-2)”.

Why only consider adults as 20 years old or older?

There is no need to compare patients that tested positive and with those that tested negative for SARS-CoV-2 as it does not add information. Thus, the first part of the Results section can be deleted. Table 1 (descriptive analysis) should be reviewed considering the correct outcomes.

Reference to “co-morbidities” or “comorbidities” should be uniformed throughout the manuscript.

Information on data sources and quality should be clearly stated, as well as how missing data was handled (even if in supplementary material).

Regarding inequalities analysis, other methods could be considered. For example, why consider 50% on poverty as the cut-off instead of quintiles?

To estimate causation, other options might be considered or discussed in the Discussion (as limitations); e.g. directed acyclic graphs. Were all the analyzed variables included in the regression analyses? Any kind of univariate analysis firstly? For the logistic regression, 95% CI must be presented.

Table 2 – It should be self-explanatory. 95% CI should be presented. The presentation could also be improved. Outcomes should be (1) hospitalization, (2) intubation and (3) death.

Discussion

Discussion should be more focused on the aforementioned aims (risk factors, including socioeconomic, for these outcomes) of this study, including comparison with the existing literature.

Other limitations should be clearly presented (e.g. ecological fallacy of considering municipalities as socioeconomic indicators instead of individual information).

Reviewer #3: In this paper, using data from the national epidemiological surveillance system, authors analyzed individuals with a positive result to estimated odds of hospitalization, intubation and death, based on pre-existing non-communicable diseases and socioeconomic indicators.

The analysis is pertinent and appropriate to draw the attention of the authorities of a country, whose health system will face important challenges in the severity of comorbid conditions and in the type of treatment that patients should receive.

Minor comments:

Reference 6 is neither complete nor in the journal format. Maybe it refers to the reference:

Barquera S, Campos-Nonato I, Hernández-Barrera L, Pedroza A, Rivera-Dommarco JA. Prevalencia de obesidad en adultos mexicanos, 2000-2012 [Prevalence of obesity in Mexican adults 2000-2012]. Salud Publica Mex. 2013;55 Suppl 2:S151-60. Spanish. PMID: 24626691.

Some typographical errors are identified in reference 12. Correct please.

Reviewer #4: Gutierrez and Bertzozzi provide a descriptive analysis of the COVID-19 pandemic in terms of the comorbidities and particularities of care and poverty amongst Mexicans suspected for COVID-19. The paper is well-written and the results are confirmatory of previously published studies (doi:10.1016/j.orcp.2020.06.001 , doi:10.1210/clinem/dgaa346/5849337, doi:10.1101/2020.05.11.20098145). A novel finding of this work is the association of poverty with COVID-19 outcomes; this confirms findings of a previous study which linked the social-lag index to increased risk of adverse outcomes for older adults in Mexico (doi:10.1093/gerona/glaa163). The authors are encouraged to contextualize their findings in light of these previous works and address the following concerns:

1. Authors incorporate multilevel data regarding poverty status and comment that "In order to take into account local epidemic dynamics, estimations assumed that observations are clustered at the municipality level"; however, no specific description of the statistical technique utilized is being referred. Was this a mixed effects logistic model? Please specify.

2. In the discussion section authors refer that severity of disease cannot be specified given available data but that is inaccurate. Previous definitions of severe COVID-19 have been based on composite outcomes of death, ICU admission and intubation, all of which are available for the Mexican Health Ministry Dataset (doi:10.1001/jamainternmed.2020.2033). Please comment on this and consider including an outcome related to severe COVID-19.

3. A main concern of the present work is the definition of inequities. Whilst inarguably, poverty is a main driver of social inequity, there are additional factors which condition in equity and are better captured by other metrics measured by CONEVAL (http://sticerd.lse.ac.uk/dps/case/cp/CASEpaper205.pdf, https://www.coneval.org.mx/Medicion/IRS/Paginas/Que-es-el-indice-de-rezago-social.aspx). Authors are concouraged to either consider additional multidimensional metrics to be able to fully capture inequality or refer to their findings in relation to poverty.

4. Authors should comment on how the approach to case tracing and confirmation in Mexico might bias their results towards high-risk cases. Given that most cases which are being tested are symptomatic cases from key risk groups, interpretation of findings should be framed in this sense.

5. In the statistical analysis section it is asserted that "In addition to demographic characteristics and comorbidities, we also included state of residence to control for variations related to differences between states". This contradicts the previous comment that obervations were clustered to accunt for local dynamics. Simply adjusting for state of occurence controls some variability but the ideal model would incorportate this as a random effect within a mixed effects framework. These differences are relevant and a mixed effect model would effectively control these variabilities.

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Reviewer #1: Yes: James Galloway

Reviewer #2: No

Reviewer #3: No

Reviewer #4: No

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Decision Letter 1

Brecht Devleesschauwer

15 Sep 2020

PONE-D-20-15813R1

Non-communicable diseases and inequalities increase risk of death among COVID-19 patients in Mexico

PLOS ONE

Dear Dr. Gutierrez,

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 submit your revised manuscript by Oct 30 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're 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.

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). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

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

We look forward to receiving your revised manuscript.

Kind regards,

Brecht Devleesschauwer

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Thank you for addressing the reviewer and editorial comments. Reviewer #2 raised some additional minor issues, which could be addressed in a final revision round.

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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 #2: (No Response)

Reviewer #4: All comments have been addressed

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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 #2: Yes

Reviewer #4: Yes

**********

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

Reviewer #2: I Don't Know

Reviewer #4: Yes

**********

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 #2: Yes

Reviewer #4: 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 #2: Yes

Reviewer #4: 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 #2: Dear Author,

Thank you for the reviewed manuscript and for the answer to the reviewers’ comments.

I would reinforce that it would be useful to mention that a similar (without peer review process) study was also published (doi: 10.1101/2020.05.11.20098145), although with a small number of patients and not consider any socioeconomic indicator regarding poverty.

Please do not use inequality and inequity interchangeably.

Please do a proof-reading once again.

Introduction

“Other positively associated conditions that are also major contributors to the global burden of disease [7]” – it seems that it is not referring COVID-19 severity (?)

“Diabetes is the single largest contributor to disease burden in Mexico, with a prevalence of about 15% of the adult population. Hypertension is also very important, with a prevalence of 30%.” – please use references to support this

“The triad of NCD, inequality and COVID doesn’t occur in the context of a high-functioning health care system, but rather one that is characterized by fragmentation, under-funding, lax regulatory oversight and a high proportion of poorly trained health personnel. The unfortunate result is low average health care quality which translates into unacceptably high mortality rates for severe COVID-19 disease.” – this triad exist in every countries with NCD, inequalities (depending on the cut-off) and with COVID-19, which correspond to almost every countries in the world. Please rephrase.

Please reformulate the aim of the study as suggested before.

Methods and Results

Missing data – It would be important to clearly state this with more detail.

Have the quintiles such an exact distribution (i.e. 20%, 40%, …)?

Table 2 – It seems still quite confusing, e.g. stating “probability” instead of “Odds”.

Reviewer #4: Thank you for addressing my comments, the paper is now ready for publication. This study adds to other analyses published using the Mexican registry database.

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Reviewer #2: No

Reviewer #4: No

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Decision Letter 2

Brecht Devleesschauwer

28 Sep 2020

Non-communicable diseases and inequalities increase risk of death among COVID-19 patients in Mexico

PONE-D-20-15813R2

Dear Dr. Gutierrez,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Brecht Devleesschauwer

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Acceptance letter

Brecht Devleesschauwer

30 Sep 2020

PONE-D-20-15813R2

Non-communicable diseases and inequalities increase risk of death among COVID-19 patients in Mexico

Dear Dr. Gutierrez:

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.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Dr. Brecht Devleesschauwer

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: Response to reviewers v28Jul2020.docx

    Click here for additional data file. (20.8KB, docx)
    Attachment

    Submitted filename: Reply to comments 21Sep.docx

    Click here for additional data file. (15.2KB, docx)

    Data Availability Statement

    Data is available at https://www.gob.mx/salud/documentos/datos-abiertos-152127.

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