Death Risk Response of High-Altitude Resident Populations to COVID-19 Vaccine: A Retrospective Cohort Study

Abstract

Vásquez-Velásquez, Cinthya, Diego Fano-Sizgorich, and Gustavo F. Gonzales. Death risk response of high-altitude resident populations to COVID-19 vaccine: a retrospective cohort study. High Alt Med Biol. 26:37–44, 2025.

Background:

Peru had one of the highest mortality rates caused by the coronavirus disease 2019 (COVID-19) pandemic worldwide. Vaccination significantly reduces mortality. However, the effectiveness of vaccination might differ at different altitudinal levels. The study aimed to evaluate the effect modification of altitude on the association between vaccination and COVID-19 mortality in Peru.

Methodology:

A retrospective cohort, using open access databases of deaths, COVID-19 cases, hospitalizations, and vaccination was obtained from the Peruvian Ministry of Health. Deaths due to COVID-19 were evaluated in vaccinated and nonvaccinated patients. Crude (RR) and adjusted relative risks (aRR) were calculated using generalized linear models of Poisson family with robust variances. Models were adjusted for age, sex, pandemic wave, and Human Development Index. To evaluate the interaction by altitude, a stratified analysis by this variable was performed. The variable altitude was categorized as, 0–499 m (828,298 cases), 500–1,499 m (64,735 cases), 1,500–2,499 m (106,572 cases), and ≥2,500 m (179,004 cases). The final sample studied included 1,362,350 cases.

Results:

The vaccine showed a considerable reduction of death risk with the second (aRR: 0.41, 95% confidence interval [CI]: 0.38–0.44) and third doses (aRR: 0.21, 95% CI: 0.20–0.23). In the adjusted and interaction model, it can be observed that medium and high altitude present a higher risk of death compared to sea level (aRR: 2.58 and 2.03, respectively). Likewise, the two doses’ group presents an aRR:1.22 for medium altitude (1,500–2,499 m) and 1.6 for high altitude (≥2,500 m), compared with low-altitude population, suggesting that the action of vaccination at high altitude is altered by the effect of the altitude itself.

Conclusions:

Altitude might modify the protective effect of SARS-CoV-2 vaccine against COVID-19 death.

Introduction

SARS-CoV-2, the pathogen that causes COVID-19, mainly affects the respiratory tract (García-Álvarez and García-Vigil, 2020). It triggers an inflammatory process, which in severe cases, might result in a “cytokine storm,” leading to systemic failure and death (Trougakos et al., 2021). The COVID-19 pandemic produced a breakdown of the health system, reflected in the alarming numbers of infected and dead people, especially in low- and middle-income countries, such as Peru, which was one of the most affected countries worldwide, with a total 4.5 million people infected and more than 219,000 deaths (CSSE, 2023).

One of the milestones in the control of the pandemic was the prompt development of vaccines. The inoculation process began in some countries in December 2020 (WHO, 2023). Multiple studies have demonstrated the effectiveness of various vaccines in reducing the risk of death and the development of severe symptoms (Mohammed et al., 2022, Rana et al., 2023, Young et al., 2022). In Peru, vaccination against COVID-19 began on February 9, 2021 (Valladares-Garrido et al., 2022). The vaccination of vulnerable groups such as elder people and health care providers was prioritized.

Some environmental factors, such as air pollution, have been found to be associated with SARS-CoV-2 infection and COVID-19 deaths (Rybarczyk et al., 2024). In addition, other environmental factors, such as living at high altitude (HA), might be associated to SARS CoV-2 severity, possibly because of the physiological differences compared with populations living at sea level (Alarcón-Yaquetto et al., 2022). However, the response to hypoxia as evaluated by the Richalet test was similar between patients with COVID-19 and those without the disease (Louis et al., 2023).

Early at the beginning of the pandemic, an article suggested that altitude may protect against COVID-19 spread (Arias-Reyes et al., 2021). As of August 2022, an increase in altitude was associated with a decrease in COVID-19 incidence. However, HA, population density, and percentage of population in total poverty did not change the COVID-19 case-fatality rate (CFR) (Vizcardo et al., 2023). This study confirms previous finding by June 13, 2020, showing that infection with COVID-19 at HA is reduced but does not change the CFR (Segovia-Juarez et al., 2020).

In a study done in Colombia, in 2022, it was found that populations at HA had similar mortality rate and lower CFR and similar excess of mortality for COVID-19 compared with the lowland population (Araque-Rodriguez et al., 2023). This confirms the findings from another study in Colombia but in 2020, in which no protective altitude gradient against SARS-CoV-2 infection or COVID-19 mortality was found (Valverde-Bruffau et al., 2021). However, these results contradict the conclusions of a first preliminary analysis, which was based on data only from 70 municipalities throughout the country, which found an inverse relationship between altitude and the CFR (Cano-Pérez et al., 2020). Data from Cano-Pérez are from the same dataset of that used by Valverde-Bruffau et al. (2021). These contradictory results suggest the importance of an adequate design to establish the association between two variables. Some even report that the association of mortality with altitude of residence is rather U-shaped with higher deaths at low altitude (LA) and altitudes >3,500 m (Baquerizo-Sedano et al., 2023).

An extensive study in India from March 2020 to the end of 2021 showed varied patterns of protection and risk against COVID-19 incidence and fatality among the HA populations (Abbasi et al., 2023). This exemplifies the complexity of this association and probably any kind of association is due to factors already present at altitude but not due to hypoxia.

Also, data reported in China indicate that altitude of residence exerts an action as an effect modifier between COVID-19 infection and factors such as mobility, absolute humidity (AH) and absolute temperature (AT), sulfur dioxide (SO₂), carbon monoxide (CO), and daily temperature range (DTR). This suggests that effect modification of HA should be considered in association analyses (Song et al., 2022). Body mass index, hypertension, and diabetes mellitus correlated significantly with COVID-19 incidence and fatality rate at HA (p ≤ 0.05) (Abbasi et al., 2023). Similarly, in Peru, diabetes mellitus increased the mortality for COVID-19 in an altitude-dependent manner (Leon-Abarca et al., 2021).

Peru presents a great diversity in terms of altitudinal floors, having populations established from sea level to even more than 4,500 m (INEI, 2021), with around 10 million residents in HA areas. The differentiated physiological characteristics of HA resident populations could also modify the effect of vaccination, as geographical, climatological, and biochemical variables generate a differentiated response to external stimuli in these populations (Alarcón-Yaquetto et al., 2022).

These physiological differences between altitudes can be observed in studies evaluating the effect of treatment on respiratory and/or immunological diseases, as in the case of patients with asthma exposed to HA, which improve their clinical process and regulate the prostaglandin/receptor ratio, which could be altered in the pathophysiological condition at LA (Boonpiyathad et al., 2020).

There are scarce studies about the efficacy and effectiveness of vaccines in HA populations. It is expected that the vaccine presents the same effects in varying altitudes, as observed in Zimbabwe when evaluating the in vitro inhibitory activity against the growth of Plasmodium falciparum (Mlambo et al., 2006). Nonetheless, there are no studies evaluating the COVID-19 vaccine response in populations residing at different altitudes, which is of interest for countries with varying altitudes and most impacted by the pandemic, such as Peru. Thus, the present study aimed to evaluate the effect modification of altitude of residence in the association between COVID-19 vaccination and mortality for COVID-19 in the Peruvian population.

Materials and Methods

Study design

This was a retrospective cohort study comprising patients infected with SARS-CoV-2 with a 30-day follow-up frame. The retrospective cohort was constructed using the open-access databases of the Peruvian Ministry of Health (MoH). During the pandemic, MoH published different COVID-19 databases for an optimal epidemiological surveillance of the disease. Three of these databases were used in the present study, which were a SARS-CoV-2-infected patients’ dataset, a COVID-19 deceased patients’ dataset, and one dataset for COVID-19 vaccination. These databases were person based, and each registry was uniquely identified with an encrypted code based on the national identity number of each person. If a person died because of COVID-19, then this unique encrypted code would appear in the infected and dead databases. In each database, the date of the event, such as date of infection, date of death, and date of every vaccine dose received, was registered. Based in this encrypted code, the databases were merged to construct a single timeline of the disease for each registry. These databases also included other information, such as sex, age, district of residence, hospitalization, and intensive care unit (ICU) admission.

The period selected for the study was from June 1, 2021, to July 31, 2022, which covers almost the entire vaccination schedule in the Peruvian territory, making it possible to delimit and observe the effect of immunization.

Study population and selection

The study population met the following inclusion criteria: data from individuals with Peruvian nationality, residing in the district where the case or death was registered. Exclusion criteria were applied: records from individuals under 18 years of age; records without information on province and district of residence; date of diagnosis of infection; and incongruent date of hospitalization, vaccination, or death (before the date of diagnosis) (Fig. 1). The final study sample consisted of 1,362,350 cases.

FIG. 1.

Flowchart of sample selection.

Study variables

COVID-19 vaccination status: obtained from the database of inoculations of the MoH (MINSA, 2023).

COVID-19 death: The outcome was defined as being registered in the COVID-19 death database within the 30 days after diagnosis of SARS-CoV-2 infection reported in the infected patients’s database. If the person did not appear in the death database, or if the date was after 30 days since the infection date, it was considered as a “no-death” outcome. The variable death due to COVID-19 was constructed using information obtained from the database of the National Death System of the MoH (MINSA, 2021).

Altitude of residence: This was defined as the altitude where those infected by SARS-CoV-2 resided, assigned according to the declared district of residence in the infection database. Altitude data was provided by the National Institute of Statistics and Informatics. The variable altitude was categorized into four levels, 0–499 m (828,298 cases), 500–1,499 m (64,735 cases), 1,500–2,499 m (106,572 cases), and ≥2,500 m (179,004 cases).

Covariates: The biological variables considered were sex and age. The variables related to the health context were hospitalization, ICU admission, and pandemic wave, and as a sociodemographic variable, the Human Development Index were included.

Statistical analysis

The raw database was constructed in Excel, version 2017. For the analysis of the database, the STATA statistical package version 18 (STATACorp, Texas USA, RRID: SCR_012763) was used.

The strategy to avoid data loss was by inquiring in the NetLab v2.0 computer system, which has the symptomatologic record of each patient who has undergone molecular testing and, for the case of antigenic and rapid tests, was reviewed in the SIS-COVID computer system; both accesses were requested to the MoH. In the case of incongruent data, it is estimated that the percentage is tolerable, around 13%, as the national information is available, and the preliminary amount obtained is more than 1,300,00 observations.

For the descriptive analysis, the numerical variables were expressed as averages and standard deviations. Categorical variables were expressed as absolute and relative frequencies. For the bivariate analysis, the evaluation of numerical variables was performed by applying the Student’s t-test for independent samples or the Mann–Whitney U test. For the evaluation of categorical variables, the Chi-square test of independence was applied.

To determine the association between vaccine and mortality for COVID-19, generalized linear models of the Poisson family, log link, and robust variances were constructed. Crude and adjusted relative risks (aRR) were expressed. The variables of sex, age, pandemic wave, and Human Development Index were included in the adjusted model. Then, to evaluate if altitude of residence acts as an effect modifier, it was included as an interaction term in the COVID-19 death—vaccine model. Lastly, an analysis stratified by vaccine dose was performed to evaluate the effect of vaccination in each altitude, and a second analysis stratified by altitude was performed to evaluate the effect of altitude on each vaccine dose. The assumptions required by each statistical test were developed. A value of p < 0.05 was considered statistically significant. Likewise, 95% confidence intervals (CI) were reported.

Ethical aspects

This protocol was registered in the Decentralized Research Information and Monitoring System (SIDISI No.:210784) and was evaluated and approved by the Universidad Peruana Cayetano Heredia’s Institutional Review Board, with Certificate No. 135-12-23. During the implementation of the study, the ethical principles outlined in the Declaration of Helsinki were respected.

Results

Table 1 shows the characteristics of the study sample (n = 1,362,350), of which 726,041 (53.29%) were women, and the average age was 40.43 ± 15.70 years. The sample was mostly distributed in the lowest altitude category from 0 to 499 m, with a total of 828,298 (70.51%) people and at ≥2,500 m with 179,004 (15.07%) inhabitants. In the intermediate altitudes were observed 14.42% of the cases (n = 171,307). A total of 647,598 people had only two vaccine doses (54.50%), followed by the third dose with 319,380 (26.88%) participants. Only 10,673 (0.90%) participants were hospitalized, and 1,935 (0.16%) were admitted in the intensive care unit. A total of 16,096 people was reported as dead by COVID-19, representing 1.18% of the total sample.

Table 1.

Characteristics of the Sample Studied (n = 1,362,350).^a Population Infected by SARS-CoV-2 Nationwide in the Period June 1, 2021, to July 31, 2022

Characteristics	n (%)
Sex
Female	726,041 (53.29)
Male	636,309 (46.71)
Age (years)^b	40.43 ± 15.70
Altitude of residence (m)
0–499	828,298 (70.51)
500–1,499	64,735 (5.45)
1,500–2,499	106,572 (8.97)
≥2,500	179,004 (15.07)
COVID-19 vaccination dose
Unvaccinated	176,835 (14.88)
1	44,336 (3.73)
2	647,598 (54.50)
3	319,380 (26.88)
Hospitalization
Yes	10,673 (0.90)
No	1,177,490 (99.10)
ICU admission
Yes	1935 (0.16)
No	1,186,228 (99.84)
Pandemic wave
Second	106,429 (8.96)
Third	1,081,734 (91.04)
Human Development Index
Low	150,247 (12.65)
Normal	536,695 (45.17)
Medium	338,153 (28.46)
High	163,068 (13.72)
Death by COVID-19
Yes	16,096 (1.18)
No	1,346,254 (98.82)

Some variables may sum to less than n = 1,362,350, because of missing data.

Mean ± standard deviation.

ICU, intensive care unit; m, meters.

In Table 2, it can be observed that at 0–499 m and at ≥2,500 m, there were a greater number of cases with 0, 1, 2, 3, and 4 doses of vaccines, hospitalization cases, ICU occupations, CFR, and deaths. In the second wave of COVID-19, no differences with respect to altitude were observed for the number of unvaccinated deceased/number of unvaccinated infected according to second or third wave of infection. The third wave of infection showed a higher proportion of deaths in subjects without vaccination over the total number of infected persons without vaccination (0.049; 95% CI: 0.028–0.033). Finally, for the CFR, the life at higher altitude (≥2,500 m) implied a higher value with 1.21 (95% CI: 1.16–1.27) per 100 infected persons, significantly higher than at 0–499 m (0.87; 95% CI: 085–0.89), or than at 1,500–2,499 m (0.85; 95% CI: 0.79–0.91).

Table 2.

Characteristics of the Sample Studied by Altitudinal Level. Population Infected by SARS-CoV-2 Nationwide During June 1, 2021, to July 31, 2022

Altitude (m)	0dose	1dose	2doses	3doses	Hospitalization	ICU admission	Death	Ratio2nd wave	Ratio3rd wave	CFR
0–499	126,058	32,031	496,486	255,315	6,787	1,380	7,900	0.024(0.023–0.026)	0.44 (0.042–0.045)	0.87(0.85–0.89)
500–1,499	13, 895	3,472	34,367	13,001	1,256	207	691	0.026(0.023–0.030)	0.040 (0.035–0.044)	1.08(1.00–1.16)
1,500–2,499	17,356	3,607	61, 501	24,108	705	78	901	0.023 (0.020–0.026)	0.049 (0.044–0.054)	0.85(0.79–0.91)
≥2,500	35, 009	9,222	93,474	41, 299	3,257	496	2,147	0.030 (0.028–0.033)	0.049 (0.046–0.053)	1.21(1.16–1.27)

Ratio: Number of unvaccinated deceased/number of unvaccinated infected by pandemic wave (95% confidence interval).

CFR: Number of deaths/Number of infected per 100 persons according to altitudinal level (95% confidence interval).

CFR, case-fatality rate.

In the bivariate Poisson regression, vaccination showed a significant reduction in COVID-19 mortality risk from the first dose and with a lower risk in the second and third dose of vaccine. Altitudes above 500 m were associated with a higher death risk, with the highest relative risk (RR) at 500–1,999 m; from 1,500 to ≥2,500 m, a higher RR was also observed with respect to reference value at 0–499 m but lower compared with the 500–1,999 m category (Table 3).

Table 3.

Analysis Between Vaccination and Death by COVID-19 and the Interaction with Altitude of Residence

	Bivariate analysis			Adjusted model^a			Adjusted model^a with interaction
	RR	95% CI	p	RR	95% CI	p	RR	95% CI	p
COVID-19 vaccination dose
Unvaccinated	Ref.			Ref.			Ref.
1	0.36	(0.33–0.39)	<0.001	1.17	(1.07–1.28)	<0.001	1.22	(1.09–1.37)	0.001
2	0.08	(0.07–0.08)	<0.001	0.43	(0.40–0.46)	<0.001	0.41	(0.38–0.44)	<0.001
3	0.14	(0.13–0.15)	<0.001	0.21	(0.20–0.23)	<0.001	0.21	(0.20–0.22)	<0.001
Altitude of residence
0–499 m	Ref.			Ref.			Ref.
500–1,499 m	5.71	(5.29–6.18)	<0.001	1.99	(1.82–2.17)	<0.001	1.94	(1.74–2.15)	<0.001
1500–2,499 m	3.68	(3.43–3.95)	<0.001	2.50	(2.32–2.69)	<0.001	2.58	(2.35–2.82)	<0.001
≥2,500 m	4.94	(4.71–5.19)	<0.001	2.15	(2.03–2.27)	<0.001	2.03	(1.90–2.18)	<0.001
Vaccination ## Altitude
0 dose^a 0–499 m							Ref.
0 dose^a 500–1,499 m							1.94	(1.74–2.15)	<0.001
0 dose^a 1,500–2,499 m							2.58	(2.35–2.82)	<0.001
0 dose^a ≥2,500 m							2.04	(1.90–2.17)	<0.001
1 dose^a 0–499 m							1.22	(1.09–1.37)	<0.001
1 dose^a 500–1,499 m							1.65	(1.21–2.26)	0.002
1 dose^a 1,500–2,499 m							2.46	(1.78–3.39)	<0.001
1 dose^a ≥2,500 m							2.55	(2.18–2.98)	<0.001
2 doses^a 0–499 m							0.41	(0.38–0.44)	<0.001
2 doses^a 500–1,499 m							1.00	(0.84–1.20)	0.935
2 doses^a 1,500–2,499 m							0.94	(0.80–1.09)	0.428
2 doses^a ≥2,500 m							1.07	(0.97–1.19)	0.168
3 doses^a 0–499 m							0.21	(0.19–0.22)	<0.001
3 doses^a 500–1,499 m							0.44	(0.34–0.55)	<0.001
3 doses^a 1,500–2,499 m							0.57	(0.47–0.68)	<0.001
3 doses^a ≥2,500 m							0.44	(0.38–0.49)	<0.001

Adjusted for sex, age, hospitalization, ICU admission, pandemic wave, Human Development Index, and altitude of residence.

CI, confidence interval; Ref., reference group; RR, relative risk.

In the adjusted model without interaction by altitude, vaccination significantly reduced death risk but from the second dose (RR: 1.17, 0.43, and 0.21 for 1, 2, and 3 doses, respectively, compared with the unvaccinated [p < 0.001]). In the interaction model, it was found that the first dose in every altitude (including the 0–499 m group) was associated with a higher mortality risk compared with the unvaccinated case at sea level (Table 3). Three doses of vaccine were associated with a lower death risk compared with two doses. This was significantly reverted in the second and third doses of vaccine, in which the 0–499 m group showed a reduced mortality risk, while for the other altitudes ≥500 m, the RR was significantly higher than at 0–499 m; the third dose was found to increase significantly the mortality risk in every altitude group compared with the value at 0–499 m (Table 3).

In the stratified analysis as seen in Table 4, the COVID-19 vaccine showed a significant protective effect starting with the second dose, at 0–499 m category (RR: 0.41, 95% CI: 0.37–0.44), and much lower with three doses (RR = 0.21; 0.19–0.22). For the other three altitudinal categories (500–1,499; 1,500–2,499; and ≥2,500 m), vaccine significantly increased COVID-19 mortality risk at the first dose (≥2500 m) (RR: 1.24, 95% CI: 1.06–1.46). Protection at any altitude was higher at second dose, and the protection was more increased at the third dose of vaccine.

Table 4.

Stratified Analysis According to the Number of Inoculated Doses and Altitude of Residence, Outcome Variable Death by COVID-19

Stratified analysis by administered vaccines
Vaccination(dose)	0–499 m			500–1,499 m			1,500–2,499 m			≥2,500 m
Vaccination(dose)	RR^a	95% CI	p	RR^a	95% CI	p	RR^a	95% CI	p	RR^a	95% CI	p
0	Ref.			Ref.			Ref.			Ref.
1	1.23	(1.09–1.38)	<0.001	0.89	(0.65–1.22)	0.489	0.93	(0.67–1.29)	0.678	1.24	(1.06–1.46)	0.008
2	0.41	(0.37–0.44)	<0.001	0.54	(0.44–0.69)	<0.001	0.33	(0.26–0.41)	<0.001	0.54	(0.48–0.61)	<0.001
3	0.21	(0.19–0.22)	<0.001	0.24	(0.19–0.32)	<0.001	0.21	(0.17–0.26)	<0.001	0.23	(0.20–0.27)	<0.001

Stratified analysis by altitude of residence
Altitude level (m)	0 dose			1 dose			2 doses			3 doses
Altitude level (m)	RR^a	95% CI	p	RR^a	95% CI	p	RR^a	95% CI	p	RR^a	95% CI	p
0–499	Ref.			Ref.			Ref.			Ref.
500–1499	1.91	(1.72–2.13)	<0.001	1.35	0.93–1.96	0.11	2.03	1.67–2.48	<0.001	2.22	1.69–2.91	<0.001
1500–2499	2.58	(2.36–2.83)	<0.001	1.89	1.33–2.67	<0.001	2.08	1.77–2.46	<0.001	2.92	2.42–3.52	<0.001
≥2500	2.07	(1.93–2.22)	<0.001	2.00	1.61–2.49	<0.001	2.1	1.86–2.38	<0.001	2.31	1.98–2.69	<0.001

Adjusted for sex, age, hospitalization, ICU admission, pandemic wave, Human Development Index.

On the contrary, in the analysis stratified by altitude of residence shown in Table 4, all altitude categories ≥500 m were associated with a higher mortality risk for COVID-19 compared with the 0–499 m category (reference group) without vaccination and in every vaccine dose group, with no clear monotonic behavior.

Discussion

The aim of the present study was to evaluate the effect modification of altitude of residence on the association between COVID-19 vaccination and mortality for COVID-19 in Peru, during June 1, 2021, to July 31, 2022. In the adjusted model (without the interaction term), there was a clear death risk reduction for the second and third doses of the vaccine, but not for the first dose, assessed in the total population. Other studies have found positive effects of vaccination even with a single dose, reducing the development of severe COVID-19 (Sadoff et al., 2021), as well as hospitalization and death (WHO, 2020). A meta-analysis found an effectiveness of combined vaccination, use of different vaccine laboratories, and for COVID-19-related mortality of 68% (hazard ratio [HR] = 0.32) and 92% (HR = 0.08) for the first and second doses, respectively. It is possible that this difference is due to the decrease in vaccine efficacy with the passage of days postinoculation, because of the response of the pathogen in evading the immune system (Nordström et al., 2022). This variable was not assessed in our study.

Another study using nationwide data evaluated the relative vaccine efficacy (RVE) of the booster dose, equivalent to the third dose; they found an overall RVE of 87.2% (84.2%−89.7%) (Silva-Valencia et al., 2022). We found a similar association in the adjusted regression in general population, with 79% (75%−82%) protection upon receiving the booster dose. This might indicate that the vaccine was effective regardless of the SARS-CoV-2 variant, as the first study covered the period during the Omicron wave, while our study considered pandemic waves two and three, with circulating variants Lambda and Delta variants with greater impact and virulence within the national territory (Fano-Sizgorich et al., 2023).

In Peru, about 10 million people live above 2,000 m, and this population shows a series of physiological differences, from changes in hematological, endocrine, and metabolic parameters. These changes are mainly due to hypoxia and environmental conditions at each altitudinal level. It is known that the higher the altitude, the higher the frequency of chronic mountain sickness (Villafuerte et al., 2022), which is characterized by the development of symptoms, such as headache (Carod-Artal, 2014), erythrocytosis, and cardiac (Zhu et al., 2023) and respiratory problems because of the stimulation of inflammation, which, in turn, increases owing to increased testosterone synthesis, causing the activation of the cytokine stream. A study in an animal model found that exposure to HA generates an increase in the expression of proinflammatory cytokines such as interleukin-8 and tumor necrosis factor-alpha (Zhu et al., 2023). This series of predominant events at HA might explain the decrease in the protective effect of vaccination on death by COVID-19. Neither physiological nor genetic adaptation to HA environment might interfere with therapeutic mechanisms of nirmatrelvir−ritonavir (Lu et al., 2023).

The projected results show that altitude of residence modified the association between vaccination and mortality for COVID-19, and it is recommended that follow-up studies of the vaccinated population at HA continued to be monitored at different periods to obtain conclusive values for this low response to vaccination at higher altitudes.

The strengths of the study were the different ranges of altitude studied from sea level to 4,500 m. This is the first time in which the response to vaccines has been studied at different altitudes.

The limitation of the study was that we were unable to control for the potential confounding effect of comorbidities such as diabetes and chronic cardiovascular disease. The prevalence of diabetes mellitus and chronic cardiovascular disease is lower at HA than at LA (Rocca et al., 2021). Patients with diabetes mellitus had a 21.8% higher prevalence of COVID-19 (Leon-Abarca et al., 2021). Obesity was associated with low anti-SARS-CoV-2 spike immunoglobulin G antibody titers following three-dose vaccination in type 2 diabetes. Obese patients with type 2 diabetes may have attenuated vaccine efficacy and require additional vaccination; continuous infection control should be considered in such patients (Takahashi et al., 2024). This could not affect our study, as obesity and diabetes prevalences were lower at HA (Maxfield et al., 2024, Rocca et al., 2021, Takahashi et al., 2024, Vena et al., 2024). Another limitation is that a cofounding factor could be an underlying medical condition (hypertension, diabetes mellitus, high cholesterol, sleep apnea, hypothyroidism, obesity, etc.), socioeconomic status, or people who had COVID-19 infection before vaccine or not. It is possible that the vaccine efficacy is different among people who had COVID-19 and recovered versus those who had no COVID-19 at all. Although hypertension, diabetes mellitus, high cholesterol, hypothyroidism, and obesity are confounding factors, they show lower rates at HA than at LA. On the contrary, it is possible that the association might vary based on vaccine technology, so future studies should consider this evaluation.

Conclusion

In conclusion, altitude might modify the association between vaccination and mortality for COVID-19. Life at HA is also a risk factor in the first and second doses compared with the risks at 0–499 m, and the effect of vaccination reducing mortality for COVID-19 was observed only at the second and third doses. Altitude may have a modifiable role or may modify COVID-19 vaccine efficacy.

Footnotes

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Authors’ Contributions

C.V.-V.: Conceptualization, methodology, investigation, formal analysis, and writing—original draft. D.F.-S.: Investigation and writing—reviewing and editing. G.F.G.: Conceptualization, resources, writing—reviewing and editing, visualization, supervision, project administration, and funding acquisition.

Author Disclosure Statement

The authors declare that they have no conflicts of interest.

Funding Information

D.F.-S., C.V.-V., and G.F.G. were supported by the Fogarty International Center, the National Institute of Aging, and the National Institute of Environmental Health Sciences under the Global Environmental and Occupational Health program award (award #2U2RTW010114). D.F.-S. was additionally supported by the training grant 5D43TW011502 awarded by the Fogarty International Center of the U.S. National Institutes of Health, studying Epidemiological Research at the Universidad Peruana Cayetano Heredia.

References

Abbasi

, Chanana

, Palmo

, et al. Disparities in COVID-19 incidence and fatality rates at high-altitude. PeerJ, 2023; 11:e14473; doi: 10.7717/peerj.14473

Alarcón-Yaquetto

, Figueroa-Mujica

, Valverde-Bruffau

, et al. Hematological Parameters and Iron Status in Adult Men and Women Using Altitude Adjusted and Unadjusted Hemoglobin Values for Anemia Diagnosis in Cusco, Peru (3400 MASL). Physiologia, 2022; 2(1):1–19.

Araque-Rodriguez

, Solarte

, Rojas-Roa

, et al. Altitude and COVID-19 in Colombia: An updated analysis accounting for potential confounders. Respir Physiol Neurobiol, 2023; 316:104136; doi: 10.1016/j.resp.2023.104136

Arias-Reyes

, Carvajal-Rodriguez

, Poma-Machicao

, et al. Decreased incidence, virus transmission capacity, and severity of COVID-19 at altitude on the American continent. PLoS One, 2021; 16(3):e0237294; doi: 10.1371/journal.pone.0237294

Baquerizo-Sedano

, Goni

, Sayón-Orea

, et al. A U-shaped protection of altitude against mortality and infection of COVID-19 in Peru: An ecological study. BMC Public Health, 2023; 23(1):1054; doi: 10.1186/s12889-023-15537-7

Boonpiyathad

, Capova

, Duchna

, et al. Impact of high-altitude therapy on type-2 immune responses in asthma patients. Allergy, 2020; 75(1):84–94; doi: 10.1111/all.13967

Cano-Pérez

, Torres-Pacheco

, Fragozo-Ramos

, et al. Negative Correlation between Altitude and COVID-19 Pandemic in Colombia: A Preliminary Report. Am J Trop Med Hyg, 2020; 103(6):2347–2349; doi: 10.4269/ajtmh.20-1027

Carod-Artal

. High-altitude headache and acute mountain sickness. Neurologia, 2014; 29(9):533–540; doi: 10.1016/j.nrl.2012.04.015

CSSE. COVID-19 Dashboard. 2023. Available from: https://coronavirus.jhu.edu/map.html

10.

Fano-Sizgorich

, Vásquez-Velásquez

, Orellana

, et al. Risk of death, hospitalization and intensive care unit admission by SARS-CoV-2 variants in Peru: A retrospective study. Int J Infect Dis, 2023; 127:144–149; doi: 10.1016/j.ijid.2022.12.020

11.

García-Álvarez

, García-Vigil

. Guidelines for clinical management of SARS-CoV-2 infection. Gac Med Mex, 2020; 156(6):576–583; doi: 10.24875/gmm.M21000461

12.

INEI. Población y Vivienda: Índice Temático. Perú. In: Instituto Nacional de Informática y Estadística ( Estadística

INdIy

ed.): Lima, 2021.

13.

Leon-Abarca

, Portmann-Baracco

, Bryce-Alberti

, et al. Diabetes increases the risk of COVID-19 in an altitude dependent manner: An analysis of 1,280,806 Mexican patients. PLoS One, 2021; 16(8):e0255144; doi: 10.1371/journal.pone.0255144

14.

Louis

, Pröpper

, Savina

, et al. The Impact of COVID-19 on the Response to Hypoxia. High Alt Med Biol, 2023; 24(4):321–328; doi: 10.1089/ham.2022.0156

15.

, Zhou

, Han

, et al. Nirmatrelvir-ritonavir treatment on SARS-CoV-2 viral dynamics in high altitude habitants. Lancet Reg Health West Pac, 2023; 30:100671; doi: 10.1016/j.lanwpc.2022.100671

16.

Maxfield

, Hadley

, Hruschka

. The relationship between altitude and BMI varies across low- and middle-income countries. Am J Hum Biol, 2024; 36(5):e24036; doi: 10.1002/ajhb.24036

17.

MINSA. SINADEF: Sistema Informático Nacional de Defunciones—Ministerio de Salud. 2021. Available from: https://www.minsa.gob.pe/defunciones/ [Last accessed: 10 de junio ].

18.

MINSA. Vacuna COVID-19 en el Perú. 2023. Available from: https://www.minsa.gob.pe/reunis/data/vacunas-covid19.asp [Last accessed: 10 de junio ].

19.

Mlambo

, Mutambu

, Mduluza

, et al. Antibody responses to Plasmodium falciparum vaccine candidate antigens in three areas distinct with respect to altitude. Acta Trop, 2006; 100(1–2):70–78; doi: 10.1016/j.actatropica.2006.09.012

20.

Mohammed

, Nauman

, Paul

, et al. The efficacy and effectiveness of the COVID-19 vaccines in reducing infection, severity, hospitalization, and mortality: A systematic review. Hum Vaccin Immunother, 2022; 18(1):2027160; doi: 10.1080/21645515.2022.2027160

21.

Nordström

, Ballin

, Nordström

. Risk of infection, hospitalisation, and death up to 9 months after a second dose of COVID-19 vaccine: A retrospective, total population cohort study in Sweden. Lancet, 2022; 399(10327):814–823; doi: 10.1016/s0140-6736(22)00089-7

22.

Rana

, Kant

, Kumra

, et al. An update on SARS-CoV-2 immunization and future directions. Front Pharmacol, 2023; 14:1125305; doi: 10.3389/fphar.2023.1125305

23.

Rocca

, Calderón

, La Rosa

, et al. Type 2 diabetes mellitus in Peru: A literature review including studies at high-altitude settings. Diabetes Res Clin Pract, 2021; 182:109132; doi: 10.1016/j.diabres.2021.109132

24.

Rybarczyk

, Zalakeviciute

, Ortiz-Prado

. Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach. Heliyon, 2024; 10(3):e25134; doi: 10.1016/j.heliyon.2024.e25134

25.

Sadoff

, Gray

, Vandebosch

, et al. Safety and Efficacy of Single-Dose Ad26.COV2.S Vaccine against Covid-19. N Engl J Med, 2021; 384(23):2187–2201; doi: 10.1056/NEJMoa2101544

26.

Segovia-Juarez

, Castagnetto

, Gonzales

. High altitude reduces infection rate of COVID-19 but not case-fatality rate. Respir Physiol Neurobiol, 2020; 281:103494; doi: 10.1016/j.resp.2020.103494

27.

Silva-Valencia

, Soto-Becerra

, Escobar-Agreda

, et al. Relative vaccine effectiveness of the booster dose of COVID-19 vaccine for preventing death in individuals with a primary regimen based on the BBIBP-CorV, ChAdOx1-S, or BNT162b2 vaccines during the Omicron wave in Peru: A nested case-control study using national population data. Vaccine, 2022; 40(45):6512–6519; doi: 10.1016/j.vaccine.2022.09.066

28.

Song

, Han

, Feng

, et al. High altitude Relieves transmission risks of COVID-19 through meteorological and environmental factors: Evidence from China. Environ Res, 2022; 212(Pt B):113214; doi: 10.1016/j.envres.2022.113214

29.

Takahashi

, Eguchi

, Watanabe

, et al. Reduced Immunogenicity of COVID-19 Vaccine in Obese Patients with Type 2 Diabetes: A Cross-Sectional Study. Acta Med Okayama, 2024; 78(2):185–191; doi: 10.18926/amo/66927

30.

Trougakos

, Stamatelopoulos

, Terpos

, et al. Insights to SARS-CoV-2 life cycle, pathophysiology, and rationalized treatments that target COVID-19 clinical complications. J Biomed Sci, 2021; 28(1):9; doi: 10.1186/s12929-020-00703-5

31.

Valladares-Garrido

, Zeña-Ñañez

, Peralta

, et al. COVID-19 Vaccine Effectiveness at a Referral Hospital in Northern Peru: A Retrospective Cohort Study. Vaccines (Basel), 2022; 10(5); doi: 10.3390/vaccines10050812

32.

Valverde-Bruffau

, Cárdenas

, Gonzales

. The Pathogenicity of COVID-19 Is Independent of Increasing Altitude: The Case of Colombia. Am J Trop Med Hyg, 2021; 104(2):783–785; doi: 10.4269/ajtmh.20-1465

33.

Vena

, Pigni

, Betella

, et al. COVID-19 vaccines and blood glucose control: Friend or foe? Hum Vaccin Immunother, 2024; 20(1):2363068; doi: 10.1080/21645515.2024.2363068

34.

Villafuerte

, Simonson

, Bermudez

, et al. High-Altitude Erythrocytosis: Mechanisms of Adaptive and Maladaptive Responses. Physiology (Bethesda), 2022; 37(4):0; doi: 10.1152/physiol.00029.2021

35.

Vizcardo

, Araníbar

, Munayco Escate

. High altitudes, population density, and poverty: Unraveling the complexities of COVID-19 in Peru during the years 2020-2022. Prev Med Rep, 2023; 36:102423; doi: 10.1016/j.pmedr.2023.102423

36.

WHO. Coronavirus disease (COVID-19). 2020. Available from: https://www.who.int/health-topics/coronavirus#tab=tab_1

37.

WHO. The different types of COVID-19 vaccines. 2023. Available from: https://www.who.int/news-room/feature-stories/detail/the-race-for-a-covid-19-vaccine-explained

38.

Young

, Crook

, Scott

, et al. Covid-19: Virology, variants, and vaccines. BMJ Med, 2022; 1(1):e000040; doi: 10.1136/bmjmed-2021-000040

39.

Zhu

, Duan

, Liu

, et al. The incidence and risk factors of perioperative cardiac complications in noncardiac major surgery in high-altitude areas: A prospective trial in Tibet autonomous region, China. Front Cardiovasc Med, 2023; 10:1158711; doi: 10.3389/fcvm.2023.1158711