ANALYSIS OF EXCESS,
ALL-CAUSE MORTALITY IN A POPULATION WITH HEALTH INSURANCE IN ARGENTINA, IN THE
CONTEXT OF THE COVID-19 PANDEMIC
ANÁLISIS DEL EXCESO DE MORTALIDAD POR TODAS LAS CAUSAS EN UNA
POBLACIÓN CON MEDICINA PREPAGA DE ARGENTINA EN EL CONTEXTO DE LA PANDEMIA POR
COVID-19*
Rafael José
Zamora,1 Adriana Beltramone,2 Federico Alem,3 Daniel Pryluka,4 Jorge De All,5 Alejandro
Regueiro,6 Nancy Ranum,7 Franco Mársico,8 Agustina Marconi9
* Para la
versión en castellano del artículo visite el siguiente vínculo: https://drive.google.com/file/d/1SwldOyZjR-fbGuVLmBL5t0n07FLBNiYy/view?usp=sharing
1 Physician.
Departamento de Estrategia Médica en Medicus, Ciudad de Buenos Aires. External
consultant for Latinas Global Health.
2 Departamento de Estadística in
Medicus, Ciudad de Buenos Aires.
3 Physician in Medicus, Ciudad de
Buenos Aires.
4 Physician. División de Infectología in Medicus,
Ciudad de Buenos Aires.
5 Physician. Departamento de Medicina
Interna del Sanatorio Otamendi, Ciudad de Buenos Aires.
6 Physician. División de Auditoría
Médica en Medicus, Ciudad de Buenos Aires.
7 Nurse. Head of Quality
and Information Technology, Health Services, University of Wisconsin in
Madison, USA.
8 Master in Sciences, Universidad
Nacional de José C. Paz.
9 Physician,
Epidemiology Department. University Services of Health, University of Wisconsin
in Madison.
CONTACT
INFORMATION
Rafael José
Zamora. Larrea 877, Ciudad de Buenos Aires. Tel.: (+54) 11 4129-5193. Email: rafael.zamora@medicus.com.ar.
Adriana Beltramone. Email: adriana.beltramone@medicus.com.ar.
Federico Alem. Email: federico.alem@medicus.com.ar.
Daniel Pryluka. Email: dpryluka@gmail.com.
Jorge De All. Email: jorgedeall@yahoo.com.ar.
Alejandro
Regueiro. Email: alejandro.regueiro@medicus.com.ar.
Franco Mársico. Email: franco.lmarsico@gmail.com.
Agustina
Marconi. Email: agustina.marconi@wisc.edu.
The authors state that they have no conflict of interest whatsoever.
Abstract
Introduction. The covid-19 pandemic has had a profound impact
worldwide. Argentina faced one of the highest covid-19 surges and longest
lockdowns in the world. Objective. Estimate the excess deaths from any
cause between March and August 2020 in a population with private health
insurance. Methods. We analyzed the death rate of the entire study
period (March-August 2020) and the death rate per month. We compared the
observed rates with the average expected rate and the limit of the 95%
confidence interval (CI). Normal distribution was considered for this
comparison. Results. 429 deaths were registered during the study period.
Of those, 19.1% (82/429) were identified as covid-19-related. Despite these
covid-19 related deaths, a significant increase of mortality in the overall
population was not observed in that time frame. March, April, and June showed a
significant decrease in mortality rates. However, August 2020 had a mortality
rate of 6.9 per 10,000, with an excess of mortality of 67.2% compared to a
historical average of 4.1 per 10,000 and 55.2% to the upper limit of the 95% CI
for the August months in 2015-2019 (p <0.001). This pattern occurred
primarily in the group aged 60 years and older (32.3 per 10,000 vs. 20.4 per
10,000; p <0.001). Conclusions. Even though March, April, and June
showed a decrease in mortality rates, in August 2020 we observed a significant
increase of the reported mortality in the population aged 60 and older.
Key words. Mortality, excess, covid-19,
pandemic.
Resumen
Introducción. La
pandemia de covid-19 tuvo un profundo impacto en todo el mundo. Argentina
enfrentó una de las mayores olas de covid-19 y uno de los confinamientos más
prolongados. Objetivo. Estimar el exceso de mortalidad en el período de
marzo a agosto de 2020 en los afiliados de una empresa de medicina prepaga. Materiales
y métodos. Analizamos la tasa de la mortalidad durante el periodo de
estudio (marzo-agosto de 2020) y de forma mensual. Se compararon las tasas
observadas con la tasa promedio esperada y el límite del intervalo de confianza
(IC) del 95%. Se utilizó una distribución normal para esta comparación. Resultados.
Se registraron 429 muertes en el período de estudio. El 19,1% (82/429) se
identificó como relacionado con la covid-19. A pesar de esas muertes
relacionadas con la pandemia por covid-19, no se observó un aumento significativo
de la mortalidad en la población general durante ese lapso. Los meses de marzo,
abril y junio registraron una disminución significativa en las tasas de
mortalidad. Sin embargo, agosto de 2020 tuvo una tasa de mortalidad de 6,9 por
cada 10.000 afiliados, con un exceso de mortalidad del 67,2% frente a un
promedio histórico de 4,1 por cada 10.000 y del 55,2%, con el límite superior
del IC del 95% para los meses de agosto de 2015-2019 (p <0,001). Este patrón
ocurrió principalmente en el grupo de los mayores de 60 años (32,3 por 10.000
vs. 20,4 por 10.000; p <0,001). Conclusiones. Aunque se observó una
disminución en las tasas de mortalidad en marzo, abril y junio, en agosto de
2020 observamos un aumento significativo de la mortalidad en la población mayor
de 60 años.
Palabras clave. Mortalidad, exceso, covid-19, pandemia.
ARK CAICYT: http://id.caicyt.gov.ar/ark:/s26184311/9efaqvadu
1. Introduction
In
December 2019, Wuhan city, the capital of Hubei province in China, became the
center of an outbreak of pneumonia of unknown cause. By Jan 7, 2020, a novel
coronavirus was identified as the source of this outbreak of severe acute
respiratory syndrome (1,2). Following its emergence, this novel coronavirus
(SARS-CoV-2) and the associated coronavirus disease 2019 (covid-19) rapidly
developed into a global pandemic, causing millions of cases and millions of
deaths in Europe and worldwide over the following months (3).
Notably,
there has been a remarkable uncertainty of the real number of covid-19 cases,
especially at the beginning. Initially, China estimated that only 10% to 15% of
all infections were laboratory-confirmed (4). Due to this, estimating the number of deaths caused
by covid-19 is a challenge. In fact, many questions have been raised about the
reported tallies of deaths related to covid-19 all over the world.
Vital
registration data on the cause of death are likely to underestimate the
mortality burden associated with the pandemic for several reasons (5,6). Covid-19 could be assigned to other causes of death due to the lack
of a testing policy, low rates of diagnoses at the time of death, or the
absence of uniform mortality coding (7,8). Also, deaths from unusual
covid-19 complications or complications not yet attributed to covid-19 could
lead to confusion, with potential attributions of death to other causes (9). Remarkably, covid-19 death counts do not
reflect indirect consequences of the lockdown on mortality levels (10). Factors including decreased access to health
care services, psychosocial consequences of isolation, stress, and depression (11,12,13), and economic, housing, and
food insecurity may contribute to mortality, including suicide, especially
among those living with chronic illness (14,15). On the other hand, reductions in travel, commerce and public
gathering during the pandemic may have resulted in a reduction of motor vehicle
mortality, violent deaths, and air pollution-related deaths (16,17). Estimates of excess deaths from all causes associated with the
pandemic provide a useful measure of the total mortality burden associated with
covid-19. Excess death refers to increases in mortality over what would
normally have been expected based on historical data for the period of
analysis, and the concept includes deaths which were either misclassified to
causes of death other than covid-19 or were indirectly related to the covid-19
pandemic. Using all causes of death to measure the excess mortality impact of
the covid-19 pandemic can help circumvent biases in vital statistics and
reporting lags, capturing excess deaths indirectly related to the pandemic (6).
The
aim of our study is to estimate the excess deaths due to any cause during the
March-August 2020 period in a private health insurance program in Argentina. We
also estimated the percentage of deaths related to covid-19 during the analyzed
period.
1.1 Argentina
On
March 12, 2020, a National Decree (260/2020) declared the existence of a health
emergency. This measure decreed the suspension of all international flights
from affected areas, as well as the compulsory isolation of travelers from
those “affected areas” (18). On March 19, a measure of “social, preventive and mandatory
isolation” was established from March 20 to March 31, 2020 in order to protect
public health. This mandate was extended
several times over the following months (19). In June, 2020 President Fernandez announced 18 provinces could end the
lockdown but continue with social distancing practices. The city of Buenos
Aires and its metropolitan area remained in partial lockdown and began a slow
reopening of some commercial activities (20).
1.2 Medicus
Medicus
is a private health insurance company in Argentina. It has 200,000 affiliate
members and has been part of the Argentinean private insurance market for over
45 years. Although it is a countrywide company, most of its members live in the
Buenos Aires Metropolitan Areaa. Becoming an affiliate is either
voluntary or is provided through social security from job benefits. The
distribution in each subgroup is around 50% and the benefits are the same.
Compared
to the Argentinean population distribution, where inhabitants aged 30 to 49
years old represent almost 19% of the population structure, Medicus population
has around 27% of affiliates in these age groups (21).
2. Materials and methods
We developed an
exploratory analysis of “excess of mortality” in a private health insurance
population. Because the first covid-19 case in Argentina was identified on
March 3, we developed the analysis of excess mortality between March and August
2020.
When Medicus members
die, they are immediately unsubscribed in order to stop monthly payment. We
included all individuals unsubscribed due to mortality as our observed/occurred
cases and as a proxy data for numbers of deaths in the analyzed period. For
expected cases in our analysis, we used the average data for the same period,
March-August in the five previous years (2015-2019). Data were analyzed as
total instances with 0-59 and ≥60 years old as separate subgroups. The standard
for documentation of death cases was the same throughout the analyzed period
(2015-2020). For the design and statistical comparison, we followed the World
Health Organization recommendations for rapid mortality surveillance and
epidemic response (22).
In addition, we
explored the percentage of deaths due to covid-19 and any existing excess of
mortality when excluding covid-19 death from the equation.
2.1 Excess of mortality calculation
2.1.1 Expected deaths
For
each year we looked for other external situations, such as civil wars, natural
disasters, etc., that could produce an excess of death not representing long
term trends.
Due
to the economic crisis in Argentina, between March 2015 and August 2020 the
number of Medicus members decreased by 6.6%, from 210,747 to 196,793 members.
As this reduction occurred across the Argentinean private health care sector (23), our analysis used the death rate instead of
absolute numbers. In addition to the member population reduction, we observed a
mild relative increase of 60 years and older clients over the years (from 17%
of all members in March 2015 to 19.6% in August 2020). We must point out that
the aging in the Medicus population was similar to the aging in the Buenos
Aires Metropolitan Area (21). Age-specific death rates were calculated for each age group (0-59
years and ≥60 years). Displaying the data in this fashion pinpoints at least
one population sub-group (those over age 60), known to be especially vulnerable
to covid-19. We defined death rate as the total deaths per 10,000 in the
general population.
2.1.2 Occurred deaths
We used the number of
dead Medicus members from March 2020 to August 2020 as the cases to obtain the
rates.
We
analyzed the death rate per the entire period and per month. We estimated the
monthly mortality ratio using the number of deaths registered in the prior five
years of data (2015-2019) and we compared the observed rates with the average
expected rate and the upper limit of the 95% confidence interval (CI). Due to
the high number of deaths, Poisson distribution, usually considered the
probability distribution which better describes mortality rates, could be
approximated by the normal distribution, which is simpler (24). In this case non-significant Shapiro’s tests were
obtained for all months and for all analyzed age categories; therefore, normal
distribution was considered. We then obtained the p-value for hypothesis
testing the null hypothesis: 2020 mortality ratio does not differ from the
estimated ratio in 2015-2019, and the alternative hypothesis: 2020 mortality
ratio is different from the estimated ratio in 2015-2019. Statistical
significance for rejecting the null hypothesis was declared when p-value was
>0.05.
Due
to the short period analyzed (monthly basis), and in order to obtain simpler
parameters, we defined death rate as the total deaths per 10,000 in the
population (22). Due to the absolute numbers
observed, gender sub-analysis was not feasible.
2.2
Percentage of deaths due to covid-19 calculation
At
the beginning of the epidemic, Medicus developed an active surveillance system
that included daily follow up of symptoms, admissions to the hospital, case
recovery and covid-19 mortality. All polymerase chain reaction (PCR) diagnostic
tests were registered during the period.
We
explored the percentage of deaths due to covid-19 in March-August 2020 using
covid-19 confirmed deaths and total deaths. We also analyzed excess of
mortality in each month of the chosen period, after removing all deaths due to
covid-19. For the purpose of this analysis, only deaths that can be accurately
attributed to covid-19 were considered (those deceased in hospitals).
3. Results
3.1 Excess
of mortality
Table
1 shows the analyzed data for mortality rate per 10,000 affiliates for the
entire March-August period. For total cases, the mortality rate in 2020 was 3.6
per 10,000 and the excess of mortality, when compared to average historical
data, was 2.4%. Nevertheless, it shows no significance when compared to the 95%
CI upper limit (-2.4%; p = 0.37). For the 0-59 subgroup the death rate in 2020
was 0.3 per 10,000 and the excess of mortality, when compared to average historical
data, was -36.7% (-46.3% vs 95% CI upper limit; p = 0.34). Finally, the
subgroup 60 and older had a mortality rate of 17.3 per 10,000 and the excess of
mortality, when compared to average historical data, was -1%. That decrease was
also not significant when compared to the historical 95% CI upper limit (-6.7%;
p = 0.41).
Using
the same approach but analyzing data for the monthly periods (Table 1 and
Figure 1), we observe a significant decrease in mortality rates during March,
April, and June. However, in August 2020 we observe an increase in mortality
rate of 2.8 per 10,000 affiliates, an excess of mortality of 67.2% when
compared to average historical data. The comparison with the upper limit of the
95% CI shows that the excess of mortality in this month was statistically
significant (55.2%; p <0.001). This same pattern was seen in August for the
subgroup of 60 and older. We observed a 2020 mortality rate of 32.3 per 10,000
and an excess of mortality compared to average historical data of 58.3% and
43.6% to the upper limit of the 95% CI (20.4 and 22.5 per 10,000 affiliates,
respectively; p <0.001).
TABLE
1. EXCESS OF MORTALITY HISTORICAL COMPARISON. TOTAL AND PER MONTH IN BOTH AGE
GROUPS. MEDICUS, MARCH-AUGUST 2020 |
||||||
Age groups |
Month |
2015-2019 monthly average (95% CI) |
2020 mortality x 10.000 |
% above baseline* |
% above threshold† |
p-value |
0-59 |
March |
0,5 (0,3-0,7) |
0,2 |
-62,8% |
-73,6% |
0,09 |
April |
0,4 (0,3-0,6) |
0,3 |
-29,6% |
-49,4% |
0,23 |
|
May |
0,5 (0,3-0,7) |
0,2 |
-62,8% |
-73,5% |
0,09 |
|
June |
0,4 (0,3-0,5) |
0,2 |
-52,9% |
-61,9% |
0,02 |
|
July |
0,5 (0,3-0,7) |
0,3 |
-47,8% |
-62,3% |
0,22 |
|
August |
0,5 (0,4-0,7) |
0,7 |
27,6% |
-2,1% |
0,22 |
|
Total for 0-59 |
|
0,5 (0,4-0,6) |
0,3 |
-36,7% |
-46,3% |
0,34 |
60+ |
March |
15,6 (15,0-16,3) |
13 |
-17,1% |
-20,6% |
<0,001 |
April |
14,5 (12,6-16,5) |
10,4 |
-28,6% |
-37,1% |
0,03 |
|
May |
16 (12,9-19,1) |
18,7 |
16,6% |
-2,2% |
0,23 |
|
June |
18,9 (16,6-21,2) |
11,9 |
-36,9% |
-43,7% |
0,001 |
|
July |
19,2 (15,9-22,5) |
17,3 |
-9,7% |
-23,0% |
0,30 |
|
August |
20,4 (18,4-22,5) |
32,3 |
58,3% |
43,6% |
<0,001 |
|
Total for 60+ |
|
17,5 (16,4-18,5) |
17,3 |
-1,0% |
-6,7% |
0,41 |
Total |
March |
3,2 (3,0-4,5) |
2,7 |
-17,2% |
-22,3% |
0,02 |
April |
2,9 (2,6-3,3) |
2,3 |
-23,9% |
-31,9% |
0,03 |
|
May |
3,3 (2,8-3,8) |
3,8 |
15,5% |
-0,3% |
0,21 |
|
June |
3,7 (3,3-4,1) |
2,5 |
-33,4% |
-39,8% |
<0,001 |
|
July |
3,9 (3,3-4,4) |
3,6 |
-6,9% |
-18,1% |
0,33 |
|
August |
4,1 (3,8-4,5) |
6,9 |
67,2% |
55,2% |
<0,001 |
|
Total |
|
3,5 (3,4-3,7) |
3,6 |
2,4% |
-2,4% |
0,37 |
*Above average (2015-2019). †Above 95% upper limit CI. |
|
Figure 1. Medicus 2020 deaths by month compared to the
upper and lower limits of historical deaths (95% CI).
It
is remarkable that the highest death rate in the history of Medicus had been in
August 2014, with 4.8 deaths per 10,000 affiliates. Instead, the death rate in
August 2020 was 6.9 per 10,000, showing a 45% increase in this line item.
3.2
Percentage of deaths related to covid-19
Between
March and August 2020, Medicus registered 429 deceased clients. Of those, 19.1%
(82/429) were covid-19 related (Table 2). When removing deaths due to covid-19,
we still observe a significant excess of death for other reasons in August 2020
when comparing those 95 non-covid-19 deaths with the 85 expected deaths for the
same period in 2015-2019 and with the upper limit of the 95% CI interval of 92.
TABLE
2. EXCESS OF MORTALITY. COVID-19 AND NON-COVID-19 CASES PER MONTH. MEDICUS, MARCH-AUGUST 2020 |
|||||||
|
Total deaths |
Covid-19 deaths |
Covid-19 deaths (%) |
Non-covid-19 deaths |
Average expected deaths |
95% tail of expected deaths |
Absolute excess mortality (per 10,000)* |
March |
53 |
0 |
0% |
53 |
67 |
71 |
-0,7. |
April |
45 |
2 |
4% |
43 |
62 |
69 |
-0,8 |
May |
75 |
4 |
5% |
71 |
68 |
79 |
0,4 |
June |
49 |
5 |
10% |
44 |
77 |
86 |
-1,4 |
July |
71 |
30 |
42% |
41 |
80 |
91 |
-0,4 |
August |
136 |
41 |
30% |
95 |
85 |
92 |
2,6 |
Total |
429 |
82 |
19%† |
347 |
439 |
488 |
-0,5 |
*Absolute excess mortality = (observed-expected) *
population. †Covid-19 deaths (%)
average. |
The
active surveillance and follow up shows that all registered covid-19 deaths
were hospitalized patients. No deaths of a covid-19 positive patient was detected
in an outpatient/home setting along the study.
4. Discussion and conclusions
When
analyzing the entire period, we do not see a significant mortality increase in
either the total sample or the chosen age subgroups. Nevertheless, when using
the same approach per month, we observe a decrease of the mortality rates in
March, April, and June, probably due to the effect of lockdown. However, during
August, we observe a significant increase in reported mortality in the total
numbers. This could be linked to the loosening of the lockdown measures
observed in almost all countries including Argentina. Mathematical models show
that lockdown fatigue and relaxation could trigger an increase in
hospitalization and death as early as two weeks after the population starts
breaking the confinement (25).
The
excess of mortality after several months could be accounted for by covid-19
cases directly or by the collateral damage of the pandemic in the private
healthcare centers of Argentina. Bozovich et al. observed a decrease in
emergency room visits (75%) and hospitalizations (48%) in the first months of
the covid-19 pandemic, as well as a 62% decrease in admissions for angina
pectoris and acute coronary syndromes, a 46% decrease in admissions for stroke
and transient ischemic attack, and a 16% decrease in cancer treatments (26). Authors concluded that even though social
distancing measures were the key strategy to flatten the infection curve, the
observed decrease in medical visits and interventions could have impacted
negatively on cardiovascular, cerebrovascular, and even cancer-related
morbidity and mortality. These observations could account for the increase we
observed in non-covid deaths in August 2020.
Our
study shows no excess of mortality in the under 60-year-old population, but
demonstrates a significant excess due to the increased mortality in age 60 and
older affiliates. This is consistent with other studies conducted in the first
months of the pandemic, where the excess of mortality was associated with older
people (27,28).
Furthermore,
our analysis shows an excess of non-covid-19 mortality in August 2020.
Non-published data from Medicus shows a 75% and a 49.5% decrease in medical and
laboratory visits, respectively, from March to August 2020, when compared to
the same period of 2019. In line with the aforementioned, this could have
impacted negatively on cardiovascular, cerebrovascular, and non-communicable
events in terms of morbidity and mortality.
This
study was conducted in a small and relatively homogeneous population. As
Medicus is a private health insurance where a high percentage of clients pay
their membership voluntarily, affiliates are usually middle/high class. As we
do not have the exact day of death, we conducted a monthly analysis instead of
a weekly one, which is the one globally done and published.
Due
to the small numbers in each category, we did not analyze the excess of
mortality per gender or per smaller age groups, as part of the literature
shows. Remarkably, during the time period under analysis, two effects were
observed in the demographic distribution: a decrease of mortality in the total
population and an increase in the average age of the population. Given that the
mortality rate in the over 60-year-old group was higher than the mortality rate
of the under age 60 group, the shift of the population density towards age over
60 had a significant effect on the total mortality rate, even though a slight
decrease of the mortality rate for each age group was observed. The decrease in
the total population seen at the same time than an increase in the over age 60
portion of the population potentiates this dynamic.
Finally,
we believe that the excess of mortality analysis should be used as a mortality
surveillance strategy by decision makers. It is a simple methodology that, if
done on a regular basis, shows not only mortality directly related to covid-19,
but a broader picture of mortality patterns in a pandemic context.
5. Acknowledgements
Enrique
Vazquez, MD.
Matías
Marquez, MS in Nuclear Engineering.
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Footnotes
a Buenos Aires Metropolitan Area refers to the urban
agglomeration comprising the Autonomous City of Buenos Aires and the adjacent
24 districts in the Province of Buenos Aires. Thus, it does not constitute a
single administrative unit.