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Revista de la Facultad de Medicina Humana

Print version ISSN 1814-5469On-line version ISSN 2308-0531

Rev. Fac. Med. Hum. vol.21 no.4 Lima Oct./Dec. 2021 

Original article

Correlation between mortality due to covid-19, wealth index, human development and population density in districts of Lima Metropolitana during 2020.

Zalia E. Dorregaray-Farge1 

Alonso Soto2  3  , Internal Medicine . MSc, PhD

Jhony De la Cruz Vargas2  , Internal Medicine . MSc, PhD

1Facultad de Medicina Humana, Universidad Ricardo Palma. Lima, Perú.

2Instituto de investigaciones en Ciencias biomédicas. Facultad de Medicina Humana, Universidad Ricardo Palma. Lima, Perú.

3Departamento de Medicina. Hospital Nacional Hipólito Unanue.



To determine the correlation between mortality due to COVID-19 and incidence of poverty and district human development index (HDI) in the department of Lima.


An observational, ecological, correlational study. The population were patients who died from COVID-19 in Lima Metropolitana. We included all patients reported dead in the open data base of the Ministerio de Salud. The dependent variable was mortality due to COVID-19, calculated by dividing the number of deaths by the total district population, and the independent variables were the incidence of poverty and HDI. We carried out a secondary analysis evaluating the fatality by COVID-19. The correlation was calculated through Spearman’s non-parametric method.


13 154 people died of COVID-19 during the period between March and September, the majority was of masculine gender with an average age of 66 years. We did not find a significant correlation between mortality and incidence of poverty (rho=-0,2230; p=0,15). We found a significant correlation between mortality due to COVID-19 and HDI (rho= 0,4466; p=0,002). Mortality was correlated with population density (rho=0,7616; p=<0,001). We found a positive (rho=0,32) and significant (p=0,037) correlation between fatality and incidence of poverty. We found a significant correlation between fatality due to COVID-19 and population density (rho=0,7616; p=<0,001). We did not find a significant correlation between fatality and HDI.


Population density was a factor associated in the most consistent manner with mortality and fatality due to COVID. Poverty was associated to greater fatality, but not to greater mortality.

Keywords: Coronavirus infection; mortality; poverty; human development index (Source: MeSH NLM).


The pandemic by COVID-19 initiated towards the end of 2019 in the city of Wuhan in China1. This disease has a wide range of clinical symptoms that range from asymptomatic infection, mild deses of upper respiratory tract to a serious viral pneumonia with respiratory distress2,3. However, few research exists that evaluate the correlation between sociodemographic and economic aspects of the populations with mortality by COVID-19.

In international studies, they reference the deaths by COVID-19 are comparable and sometimes superior to the total of deaths by opioids and HIV/AIDS4. In the United States, monetary income inequality is reflected in a greater number of deaths, and this could generate differences found in the different states5. Other studies that have assessed the effect of sociodemographic indicators in rural and urban communities have found an association between socioeconomic status (relative risks [RR] of 1.42 and 1.71) and housing and transportation domains (RR 1.52 and 1.32) with a major risk of COVID-19 diagnosis and death6. Another reported consequence of inequalities is associated with the diagnostic test availability for the entire population. In the beginning of the pandemic, there were more diagnosis in the most unfavorable locations, but this changed over time, a greater number of positive cases in wealthier places began to be reported. The inequalities associated to racial factors have also played a role. For example, a double risk for COVID-19 hospitalizations among the African American population in the United States has been reported7.

A Peruvian study analyzed mortality by non-violent causes and district quintiles, showing a relation between human development index (HDI) and mortality due to COVID-19, with a greater number of deaths due to COVID-19 in quintiles with lower HDI. Although this study did not find a significant association between poverty and mortality, their findings are limited due to short observation time8.

Our study seeks to find a correlation between mortality due to COVID-19, poverty percentage, HDI, and population density in districts within Metropolitan Lima during 2020.


Design and Field of study

An observational, ecological, correlational study, conducted based on data from Metropolitan Lima

Population and Sample

The study’s population was made up of all patients that died from COVID-19 in the districts of Metropolitan Lima, for the development of this research we used a free access data base from the Ministerio de Salud del Perú available in the following link:

Variables and instruments

The principal variable was mortality due to COVID-19 and also as potential factors that could influence infection and death rates. These factors can be grouped into four principal categories: population characteristics, environmental/geographical factors, health policy, and virus-related factors.

To perform this study, we used the free-access data base from the Ministerio de Salud del Perú4,9. The HDI statistics and the district poverty rate were obtained from the report: “Herramienta de lucha contra la pobreza-Módulo Perú”.9The demographic density of the districts in Lima was obtained from the study titled: “Provincia de Lima: Compendio estadístico-2017”. This document is part of the last population census and the housing development by the Instituto Nacional de Estadística e Informática (INEI) in 201710


Once the consolidated data base was obtained by district, the descriptive analysis was carried out based on the variable type.

Statistical analysis

The descriptive analysis was carried out based on the type of variable. For quantitative variables the measures of central tendency and dispersion were used according to data normality. Frequencies and percentages were used for qualitative variables.

The correlation between mortality due to COVID-19, poverty rate, and population density was calculated using the Spearman correlation method. A value of p<0.05 was considered significant. The statistical analysis was carried out using the Stata V15 Statistical11. At the same time a multivariate analysis (robust regression) was performed.

Ethical aspects

This research uses the information from a free access data base. Patient coding makes it impossible to discover the personal data of patients who died from COVID-19. The study was approved by the comité de ética institucional from the Universidad Ricardo Palma.


We analyzed the data of 13,154 people who died due to COVID-19 in Lima from March 18th to September 30th, 2020. 70,9% (9325) of the deceased were males. The districts with greater number of deaths were San Juan de Lurigancho with 11.4% (1,499), Lima with 8.3% (1,096) and San Martin de Porres with 7.0% (919). The districts with fewer number of deaths were the districts of Punta Hermosa (9 deceased), San Bartolo (9 deceased), Punta Negra (2 deceased), Santa María del Mar (1 deceased). The districts with greater fatalities due to COVID-19 were Villa El Salvador (6.04%), La Victoria (5.12%), Chorrillos (5.07%) and San Juan de Lurigancho (4.99%). The districts with lower fatalities due to COVID-19 were Santa María del Mar (1.54%), Jesús María (1.72%) and Punta Negra (2.0%). The total number of deceased per districts are shown inTable 1. Mortality and fatality due to COVID-19 in the same period are shown inTable 2.

Table 3shows the poverty rate and human development index of each district of Lima during 2020. The poverty rate of the districts varied from 0.2% in the district of Miraflores, San Isidro up to 41.1% in the district of Pachacamac and 40.7% in Puente Piedra. With regards to the district HDI, we observe that San Isidro and Miraflores have the highest indexes with 0.809 and 0.790, respectively. The districts with lowest HDI were the districts of Puente Piedra (0.673) and Pachacamac (0.669.Table 3shows a population density of the districts in Lima according to the last census carried out by INEI. We observe that the districts of Punta Negra and Punta Hermosa have the lowest population density with 64 and 67 Inhab/Km2, respectively, while the districts of Surquillo a2the highest levels of population density with 2 6438 and 2 3202 Inhab/Km .

Mortality due to COVID-19 and its correlation with poverty, HDI, and population density

No significant correlation was found between mortality and poverty rate (rho= -0.2230; p=0.150) (Figure 1). A significant correlation was found between mortality due to COVID-19 and HDI (rho= 0.4466; p=0.002) (Figure 1). A significant correlation was found between mortality due to COVID-19 and population density (rho=0.7616; p=<0.001) (Figure 1).

Fatality due to COVID-19 and its correlation with poverty, HDI, and population density

A statistically significant correlation was found between fatality due to COVID-19 and district poverty rate (rho=0.319; p=0.037) (Figure 1). No significant correlation was found between fatality due to COVID-19 and district HDI (rho=-0.106; p=0.501) (Figure 1). A significant correlation was found between fatality due to COVID-19 and population density (rho=0.7616; p=<0.001) (Figure 1).

Additionally, a significant correlation was found between poverty rate and district HDI (rho= -0.8871; p<0,001) (Figure 1).

In the robust regression model used, only population density was a factor associated with mortality due to COVID-19 (Beta coefficient:0.06 CI95%:0.03-0.08; p<0.001). Population density was also associated with fatality due to COVID-19 (Beta coefficient: 0.06 CI95%:0.03-0.08; p<0.001). The results are shown inTable 4, therefore, it is assumed that robust regression associates much more population density with mortality and lethality, being poverty and HDI, nonsignificant variables.

Figure 1: A: Scatter plot: mortality by Covid-19 and district poverty rate. B: Scatter plot: mortality by COVID-19 and district human development index. C: Scatter plot: mortality by COVID-19 and district population density. D: Scatter plot: mortality by COVID-19 and district poverty rate. E: Scatter plot: fatality by COVID-19 and Human Development Index. F: Scatter plot: fatality by COVID-19 and district population density. G: Scatter plot: Dispersion diagram: human development index and percentage of district poverty 

Table 1.  Frequency of death by COVID-19, Lima 2020 

Frequency %
San Juan de Lurigancho 1499 11,4
Lima 1096 8,3
San Martin de Porres 919 7,0
Villa el Salvador 848 6,4
Comas 841 6,4
Ate 740 5,6
San Juan de Miraflores 569 4,3
La Victoria 556 4,2
Villa Maria del Triunfo 533 4,1
Chorrillos 480 3,6
El Agustino 428 3,3
Los Olivos 413 3,1
Santa Anita 392 3,0
Santiago de Surco 364 2,8
Rimac 362 2,8
Independencia 332 2,5
Puente Piedra 298 2,3
Carabayllo 293 2,2
Breña 233 1,8
Jjesus Maria 229 1,7
Lurigancho 168 1,3
San Miguel 168 1,3
Surquillo 162 1,2
Pueblo Libre 139 1,1
Lince 133 1,0
La Molina 124 ,9
Miraflores 113 ,9
Lurin 97 ,7
San Borja 93 ,7
San Luis 93 ,7
Magdalena del Mar 80 ,6
Pachacamac 67 ,5
Barranco 63 ,5
Ancon 56 ,4
Chaclacayo 52 ,4
San Isidro 49 ,4
Pucusana 22 ,2
Cieneguilla 18 ,1
Santa Rosa 11 ,1
Punta Hermosa 9 ,1
San Bartolo 9 ,1
Punta Negra 2 ,0
Santa Maria del Mar 1 ,0
Total 13154 100,0

Table 2.  Mortality and Fatality by COVID-19 in Lima ,2020 

Villa El Salvador 423887 12326 848 2,00 6,04
La Victoria 188619 9499 556 2,95 5,12
Chorrillos 355978 8390 480 1,35 5,07
San Juan de Lurigancho 1177755 26351 1499 1,27 4,99
Villa María del Triunfo 437992 9562 533 1,22 4,96
Breña 93111 3708 233 2,50 4,91
Santa Anita 221776 7093 392 1,77 4,86
Pueblo Libre 94010 2300 139 1,48 4,78
San Bartolo 8722 172 9 1,03 4,65
Comas 573884 15888 841 1,47 4,61
San Juan de Miraflores 412865 10740 569 1,38 4,57
Barranco 35915 1148 63 1,75 4,44
Ate 670818 14305 740 1,10 4,38
Lurín 109506 1924 97 0,89 4,37
Rímac 180260 7530 362 2,01 4,26
San Martín de Porres 744050 18260 919 1,24 4,23
Lima 267379 23359 1096 4,10 4,11
Lince 59578 2507 133 2,23 4,11
Independencia 222850 7175 332 1,49 4,06
Ancón 82677 1140 56 0,68 4,04
Pucusana 16615 503 22 1,32 3,98
Pachacámac 142133 1417 67 0,47 3,95
Surquillo 100339 3429 162 1,61 3,85
Carabayllo 400414 6596 293 0,73 3,79
Punta Hermosa 22230 159 9 0,40 3,77
El Agustino 221974 10043 428 1,93 3,69
Lurigancho 283231 3949 168 0,59 3,65
Los Olivos 351983 9671 413 1,17 3,64
Santiago de Surco 408086 8231 364 0,89 3,61
Magdalena del Mar 65139 1855 80 1,23 3,56
Puente Piedra 395819 7517 298 0,75 3,53
La Molina 160244 3060 124 0,77 3,14
Cieneguilla 39055 421 18 0,46 3,09
Chaclacayo 44157 1450 52 1,18 3,03
San Miguel 173309 4384 168 0,97 3,03
San Luis 55793 2242 93 1,67 2,99
Miraflores 108855 3170 113 1,04 2,81
Santa Rosa 37940 331 11 0,29 2,72
San Borja 127102 3447 93 0,73 2,29
San Isidro 67703 2129 49 0,72 2,11
Punta Negra 8243 100 2 0,24 2,00
Jesús María 81743 11381 229 2,80 1,72
Santa María del Mar 1142 65 1 0,88 1,54

Table 3.  Poverty rate and district growth rate in Lima, 2020 and district population density during the last census in Lima 

District Poverty (%) Human Development Index (HDI) District Population density (Inhab/Km2)
Pachacámac 41,10 ,669 Punta Negra 64.00
Puente Piedra 40,70 ,673 Punta Hermosa 67.00
Lurín 37,70 ,678 Ancón 139.00
Villa El Salvador 37,40 ,690 Santa María del Mar 173.00
Pucusana 37,10 ,692 San Bartolo 179.00
Carabayllo 35,40 ,687 Cieneguilla 207.00
Cieneguilla 35,20 ,675 Pucusana 481.00
Independencia 33,10 ,699 Lurín 496.00
El Agustino 32,60 ,690 Pachacámac 855.00
Villa María del Triunfo 32,10 ,691 Carabayllo 917.00
Ancón 32,00 ,676 Santa Rosa 921.00
San Juan de Miraflores 30,90 ,693 Lurigancho 970.00
Punta Negra 30,50 ,682 Chaclacayo 1119.00
San Juan de Lurigancho 30,40 ,685 La Molina 2734.00
San Bartolo 28,80 ,695 San Isidro 4816.00
Ate 28,40 ,695 Puente Piedra 5241.00
Comas 27,80 ,699 Villa María del Triunfo 6600.00
Punta Hermosa 26,30 ,684 Miraflores 8484.00
Santa Rosa 24,50 ,683 Ate 8515.00
Lurigancho 23,80 ,682 Chorrillos 8630.00
Santa Anita 23,60 ,693 San Juan de Lurigancho 8674.00
Rímac 20,60 ,714 Barranco 8700.00
San Martín de Porres 18,90 ,709 Santiago de Surco 10290.00
Santa María del Mar 17,50 ,680 Comas 11021.00
La Victoria 15,80 ,731 San Borja 11316.00
Lima 15,50 ,727 Lima 12088.00
Chorrillos 3,40 ,702 San Miguel 12803.00
Chaclacayo 2,80 ,702 Villa El Salvador 13594.00
Los Olivos 2,10 ,706 Rímac 13723.00
San Luis 1,20 ,719 Independencia 15135.00
Surquillo ,70 ,736 Magdalena del Mar 15215.00
Pueblo Libre ,70 ,740 El Agustino 15574.00
Breña ,70 ,736 Jesús María 15685.00
La Molina ,60 ,736 Lince 16193.00
Barranco ,60 ,742 San Luis 16751.00
Santiago de Surco ,50 ,755 San Juan de Miraflores 17342.00
San Miguel ,50 ,753 Pueblo Libre 17381.00
Magdalena del Mar ,50 ,758 La Victoria 19068.00
San Borja ,40 ,766 San Martín de Porres 19777.00
Lince ,40 ,752 Los Olivos 21080.00
Jesús María ,30 ,769 Santa Anita 22291.00
San Isidro ,20 ,809 Breña 23202.00
Miraflores ,20 ,790 Surquillo 26438.00

Table 4.  Factors associated to mortality and fatality by Covid-19 at district level 

Mortality by COVID-19 Beta coefficient p- value CI 95%
Poverty rate 0,03 0,07 -0,015;0,021
Human development index 0,46 0,90 -7,35;8,27
Population density 0,06 <0,001 0,03;0,08
Fatality by COVID-19 Beta coefficient p-value CI 95%
Poverty rate 0,2 0,205 -0,01;0,06
Human development index -6,56 0,370 -7,35;8,27
Population density 0,06 0,003 0,02;0,10


The results from this study show that mortality due to COVID-19 in the districts of Lima were greater mostly in older adults over 60 years of age and of masculine gender. In addition to this, we were able to determine that from the beginning of the pandemic until the month of September there was a positive correlation (at district level) between fatality due to COVID-19 and poverty rate at the district level. Population density was the factor associated more consistently to mortality and fatality due to COVID-19. For every increase in population density (in one thousand inhabitants), the fatality and mortality from district COVID-19 infection would increase 6%. Poverty was associated with greater fatality, but not with greater mortality.

Deaths due to COVID-19 mostly in older adults of male gender with comorbidities such as hypertension, obesity, diabetes(31-34). Prior studies such Hernández-Vásquez et al.8describe that there is an excess of mortality of 20,093 non-violent deaths and 2,979 COVID-19 confirmed deaths in the department of Metropolitan Lima during the first 24 weeks of the year 2020, where older adults, males, and in poverty were the ones who died most from this disease8.

The main results from this study show that the greater the district poverty, the greater COVID-19 fatality. This resembles prior research such as those of Ogedegbe et al., Gadson et al. and Hernández-Vásquez et al.8among others(14,15). Ogedegbe et al. showed in his study the existence of a substantial variation of COVID-19 fatality in the districts of New York city, determining that zones with greater number of people living in poverty and lower educational levels had higher rates of deaths related to COVID-199. In this same manner, Hernández-Vásquez et al. describe that the excess of deaths due to COVID-19 were greater in the districts with extreme poverty8. The study of these relationships demonstrates the importance of accessibility of district monetary resources facing the fatality of this pandemic, given that districts with less accessibility of economic resources have greater chance of counting on less infrastructure resources and human resources, reporting in turn a greater number of deaths14,15.

This study has evaluated the correlation between fatality and poverty rate at the district level. This study evaluated a smaller population compared to other studies such as Achoki et al. which evaluated countries that showed a similar situation16-18. Achoki et al. Demonstrated that fatality due to COVID-19 (in the beginning of the pandemic) was greater in impoverished countries in continents, such as Africa, and that this too could replicate in countries in Latin America, as we can observe through results from this study18,19.

The main implications in this research were the possibility to determine population groups with greater risk, where the state and health ministry can intervene and target prevention actions such as early detection of cases, of patients with greater risk of infection and mortality, as well as early referral to health establishments with greater complexity, mainly in those districts with higher poverty levels.

The main limitations from this research were related to the data base quality. It is possible that an underreporting of patients who died in the beginning of the pandemic exist, due to lack of knowledge of diagnostic criteria and delay in molecular test results, among others. Since it is an ecological study, the results should be interpreted at a district level and not necessarily at an individual level. However, we consider that the risk of ecological fallacy is highly unlikely.


COVID-19 is transmitted directly from person to person, being the cause of many complications such as a trigger of pneumonia, the transmission is among people that keep little distance from each other, this disease will travel through respiratory droplets, this disease spreads when the infected person coughs, this is why the association between mortality and fatality with population density is important, since once there is contact between people the spread is greater. This is shown in the results of robust regression, that when population density in a thousand inhabitants increases, the district fatality due to COVID-19 increased by 6%, and we can also observe that the districts with less population density had less deaths in a bivariate analysis, and it is much more associated in a robust regression, therefore we show that population density is a very relevant factor, with poverty and HDI not having any association.

The prevention of the agglomeration of people is very important in enclosed places or those with poor ventilation, since this would make the spread easier.


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Funding sources: Self-funded.

Received: June 08, 2021; Accepted: August 05, 2021

Correspondence: Zalia Elizabeth Dorregaray Farge Address: Av. Alfredo Benavides 5440, Santiago de Surco, Lima, Perú. Telephone: 999986642

Authorship contributions: The authors participated in the genesis of the idea, project design, data collection and interpretation, analysis of results and preparation of the manuscript of this research work.

Conflicts of Interest: The authors declare that they have no conflicts of interest.

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