Mortality rate

Mortality rate, or death rate,[1] is a measure of the number of deaths (in general, or due to a specific cause) in a particular population, scaled to the size of that population, per unit of time. Mortality rate is typically expressed in units of deaths per 1,000 individuals per year; thus, a mortality rate of 9.5 (out of 1,000) in a population of 1,000 would mean 9.5 deaths per year in that entire population, or 0.95% out of the total. It is distinct from "morbidity", which is either the prevalence or incidence of a disease,[2] and also from the incidence rate (the number of newly appearing cases of the disease per unit of time).

In the generic form, mortality rates are calculated as:

${\displaystyle d/p\cdot 10^{n}}$

where d represents the deaths occurring within a given time period, p represents the size of the population in which the deaths occur and ${\displaystyle 10^{n}}$is a conversion factor from fraction to some other unit (such as multiplying by ${\displaystyle 10^{3}}$to get mortality rate per 1,000 individuals).

Other specific measures of mortality include:

Measures of Mortality [3]
Crude death rate – the total number of deaths per year per 1,000 people. As of 2017 the crude death rate for the whole world is 8.33 per 1,000 (up from 7.8 per 1,000 in 2016) according to the current CIA World Factbook.[4]
Perinatal mortality rate – the sum of neonatal deaths and fetal deaths (stillbirths) per 1,000 births.
Maternal mortality ratio – the number of maternal deaths per 100,000 live births in same time period.
Maternal mortality rate – the number of maternal deaths per 1,000 women of reproductive age in the population (generally defined as 15–44 years of age).
Infant mortality rate – the number of deaths of children less than 1 year old per 1,000 live births.
Child mortality rate: the number of deaths of children less than 5 years old per 1,000 live births.
Standardized mortality ratio (SMR) – a proportional comparison to the numbers of deaths that would have been expected if the population had been of a standard composition in terms of age, gender, etc.[5]
Age-specific mortality rate (ASMR) – the total number of deaths per year per 1,000 people of a given age (e.g. age 62 last birthday).
Cause-specific mortality rate – the mortality rate for a specified cause of death.[6]
Cumulative death rate: a measure of the (growing) proportion of a group that die over a specified period (often as estimated by techniques that account for missing data by statistical censoring).[7]
Case fatality rate (CFR) – the proportion of cases of a particular medical condition that lead to death.[8][9][10]
Sex-specific mortality rate - Total number of deaths in a population of a specific sex within a given time interval[3]

Use in epidemiology

In most cases, there are few ways, if at all possible to obtain exact mortality rates, so epidemiologists use estimation to predict correct mortality rates. Mortality rates are usually difficult to predict due to language barriers, health infrastructure related issues, conflict, and other reasons. Maternal mortality has additional challenges, especially as they pertain to stillbirths, abortions, and multiple births. In some countries, during the 1920s, a stillbirth was defined as "a birth of at least twenty weeks' gestation in which the child shows no evidence of life after complete birth". In most countries, however, a stillbirth was defined as "the birth of a fetus, after 28 weeks of pregnancy, in which pulmonary respiration does not occur".[11]

Census data and vital statistics

Ideally, all mortality estimation would be done using vital statistics and census data. Census data will give detailed information about the population at risk of death. The vital statistics provide information about live births and deaths in the population.[12] Often, either census data and vital statistics data is not available. This is especially true in developing countries, countries that are in conflict, areas where natural disasters have caused mass displacement, and other areas where there is a humanitarian crisis [12]

Household surveys

Household surveys or interviews are another way in which mortality rates are often assessed. There are several methods to estimate mortality in different segments of the population. One such example is the sisterhood method, which involves researchers estimating maternal mortality by contacting women in populations of interest and asking whether or not they have a sister, if the sister is of child-bearing age (usually 15) and conducting an interview or written questions about possible deaths among sisters. The sisterhood method, however, does not work in cases where sisters may have died before the sister being interviewed was born.[13]

Orphanhood surveys estimate mortality by questioning children are asked about the mortality of their parents. It has often been criticized as an adult mortality rate that is very biased for several reasons. The adoption effect is one such instance in which orphans often do not realize that they are adopted. Additionally, interviewers may not realize that an adoptive or foster parent is not the child's biological parent. There is also the issue of parents being reported on by multiple children while some adults have no children, thus are not counted in mortality estimates.[12]

Widowhood surveys estimate adult mortality by responding to questions about the deceased husband or wife. One limitation of the widowhood survey surrounds the issues of divorce, where people may be more likely to report that they are widowed in places where there is the great social stigma around being a divorcee. Another limitation is that multiple marriages introduce biased estimates, so individuals are often asked about first marriage. Biases will be significant if the association of death between spouses, such as those in countries with large AIDS epidemics.[12]

Sampling

Sampling refers to the selection of a subset of the population of interest to efficiently gain information about the entire population. Samples should be representative of the population of interest. Cluster sampling is an approach to non-probability sampling; this is an approach in which each member of the population is assigned to a group (cluster), and then clusters are randomly selected, and all members of selected clusters are included in the sample. Often combined with stratification techniques (in which case it is called multistage sampling), cluster sampling is the approach most often used by epidemiologists. In areas of forced migration, there is more significant sampling error. Thus cluster sampling is not the ideal choice.[14]

Mortality statistics

World historical and predicted crude death rates (1950–2050)
UN, medium variant, 2012 rev.[15]
YearsCDRYearsCDR
1950–195519.12000–20058.4
1955–196017.32005–20108.1
1960–196516.22010–20158.1
1965–197012.92015–20208.1
1970–197511.62020–20258.1
1975–198010.62025–20308.3
1980–198510.02030–20358.6
1985–19909.42035–20409.0
1990–19959.12040–20459.4
1995–20008.82045–20509.7

The ten countries with the highest crude death rate, according to the 2016 CIA World Factbook estimates, are:[16]

RankCountryDeath rate
(annual deaths/1,000 persons)
1 Lesotho14.9
2 Bulgaria14.5
3 Lithuania14.5
4 Ukraine14.4
5 Latvia14.4
6 Guinea-Bissau14.1
8 Afghanistan13.7
9 Serbia13.6
10 Russia13.6

According to the World Health Organization, the ten leading causes of death in 2015 (ranked by death per 100,000 population) were:[17]

1. Ischaemic heart disease (119 per 100,000 population)
2. Stroke (85 per 100,000 population)
3. Lower respiratory infections (43 per 100,000 population)
4. Chronic obstructive pulmonary disease (43 per 100,000 population)
5. Trachea/bronchus/lung cancers (23 per 100,000 population)
6. Diabetes mellitus (22 per 100,000 population)
7. Alzheimer's disease and other dementias (21 per 100,000 population)
8. Diarrhoeal diseases (19 per 100,000 population)
9. Tuberculosis (19 per 100,000 population)
10. Road traffic accidents (10 per 100,000 population)

Causes of death vary greatly between developed and less developed countries. See list of causes of death by rate for worldwide statistics.

According to Jean Ziegler (the United Nations Special Rapporteur on the Right to Food for 2000 to March 2008), mortality due to malnutrition accounted for 58% of the total mortality in 2006: "In the world, approximately 62 millions people, all causes of death combined, die each year. In 2006, more than 36 million died of hunger or diseases due to deficiencies in micronutrients".[18]

Of the roughly 150,000 people who die each day across the globe, about two thirds—100,000 per day—die of age-related causes.[19] In industrialized nations, the proportion is much higher, reaching 90%.[19]

Economics

Scholars have stated that there is a significant relationship between a low standard of living that results from low income and increased mortality rates. A low standard of living is more likely to create situations where malnutrition is more common, which can in turn cause the impacted people to become more susceptible to disease and an increased likelihood of dying from these diseases. People who have a lower standard of living are also more likely to face issues such as a lack of hygiene and sanitation, the increase of exposure to and the spread of disease, and a lack of access to proper medical care and facilities. Poor health can in turn contribute to low and reduced incomes, which can create a loop known as the health-poverty trap.[20] Indian economist and philosopher Amartya Sen has stated that mortality rates can serve as an indicator of economic success and failure.[21][22]:27, 32

Historically, mortality rates have been adversely affected by short term price increases. Studies have shown that mortality rates increase at a rate concurrent with increases in food prices. These effects have a greater impact on vulnerable, lower-income populations than they do on populations with a higher standard of living.[22]:35,36,70

In more recent times, higher mortality rates have been less tied to socio-economic levels within a given society, but have differed more between low and high-income countries. It is now found that national income, which is directly tied to standard of living within a country is the largest factor in mortality rates being higher in low-income countries.[23]

These rates are especially pronounced for children under the age of 5-years old, particularly in lower-income, developing countries. These children have a much greater chance of dying of diseases that have become very preventable in higher-income parts of the world. The instances of these children dying of things like malaria, respiratory infections, diarrhea, perinatal conditions, or measles are much more pronounced in developing nations. Data shows that after the age of 5 these preventable causes level out between high and low-income countries. The only cause of death that affects people aged 30-59 at a significantly higher rate in low income.[24]

References

1. Porta, M, ed. (2014). "Death rate". A Dictionary of Epidemiology (5th ed.). Oxford: Oxford University Press. p. 69. ISBN 978-0-19-939005-2.
2. Porta, M, ed. (2014). "Morbidity rate". A Dictionary of Epidemiology (5th ed.). Oxford: Oxford University Press. p. 189. ISBN 978-0-19-939005-2.
3. "Principles of Epidemiology | Lesson 3 - Section 3". www.cdc.gov. Retrieved 2017-12-18.
4. "CIA World Factbook. (Search for 'People and Society')". 2016.
5. Everitt, B.S. The Cambridge Dictionary of Statistics, CUP. ISBN 0-521-81099-X
6. Sun, Hongbing (2017-01-12). "Temperature dependence of multiple sclerosis mortality rates in the United States". Multiple Sclerosis Journal. 23 (14): 1839–1846. doi:10.1177/1352458516688954. ISSN 1352-4585. PMID 28080218.
7. Porta, M, ed. (2014). "Cumulative death rate". A Dictionary of Epidemiology (5th ed.). Oxford: Oxford University Press. p. 64. ISBN 978-0-19-939005-2.
8. Porta, M, ed. (2014). "Case fatality rate". A Dictionary of Epidemiology (5th ed.). Oxford: Oxford University Press. p. 36. ISBN 978-0-19-939005-2.
9. Benson, Michael D. (2017-08-17). "Amniotic fluid embolism mortality rate". Journal of Obstetrics and Gynaecology Research. 43 (11): 1714–1718. doi:10.1111/jog.13445. ISSN 1341-8076. PMID 28817205.
10. Turner, Paul J.; Jerschow, Elina; Umasunthar, Thisanayagam; Lin, Robert; Campbell, Dianne E.; Boyle, Robert J. (2017-09-01). "Fatal Anaphylaxis: Mortality Rate and Risk Factors". The Journal of Allergy and Clinical Immunology: In Practice. 5 (5): 1169–1178. doi:10.1016/j.jaip.2017.06.031. PMC 5589409. PMID 28888247.
11. Loudon, Irvine (1992-11-05). Death in Childbirth: An International Study of Maternal Care and Maternal Mortality 1800-1950 - Oxford Scholarship. Oxford University Press. doi:10.1093/acprof:oso/9780198229971.001.0001. ISBN 9780191678950.
12. Timæus, Ian M. (1991). "Measurement of Adult Mortality in Less Developed Countries: A Comparative Review". Population Index. 57 (4): 552–568. doi:10.2307/3644262. JSTOR 3644262.
13. Graham, W.; Brass, W.; Snow, R. W. (May 1989). "Estimating maternal mortality: the sisterhood method". Studies in Family Planning. 20 (3): 125–135. doi:10.2307/1966567. ISSN 0039-3665. JSTOR 1966567. PMID 2734809.
14. Migration, National Research Council (US) Roundtable on the Demography of Forced (2002). ESTIMATING MORTALITY RATES. National Academies Press (US).
15. "UNdata - record view - Crude death rate (deaths per 1,000 population)". data.un.org.
16. "The World Factbook — Central Intelligence Agency". www.cia.gov.
17. "Top 10 causes of death". World Health Organization.
18. Jean Ziegler, L'Empire de la honte, Fayard, 2007 ISBN 978-2-253-12115-2, p.130.
19. Aubrey D.N.J, de Grey (2007). "Life Span Extension Research and Public Debate: Societal Considerations" (PDF). Studies in Ethics, Law, and Technology. 1 (1, Article 5). CiteSeerX 10.1.1.395.745. doi:10.2202/1941-6008.1011. Retrieved August 7, 2011.
20. "Health, Income, & Poverty: Where We Are & What Could Help". Health Affairs. October 4, 2018. doi:10.1377/hpb20180817.901935/full/ (inactive 2019-08-20). Retrieved 2019-07-31.
21. Sen, Amartya (1998). "Mortality as an Indicator of Economic Success and Failure". The Economic Journal. 108 (446): 1–25. doi:10.1111/1468-0297.00270. ISSN 0013-0133. JSTOR 2565734.
22. Bengtsson, Tommy; Campbell, Cameron; Lee, James Z. (2004). Life under pressure : mortality and living standards in Europe and Asia, 1700-1900. Cambridge, Mass.: MIT. ISBN 9780262268097. OCLC 57141654.
23. Preston, Samuel H. (2007-06-01). "The changing relation between mortality and level of economic development". International Journal of Epidemiology. 36 (3): 484–490. doi:10.1093/ije/dym075. ISSN 0300-5771. PMC 2572360. PMID 17550952.
24. Bengtsson, Tommy, et al. Life under Pressure: Mortality and Living Standards in Europe and Asia, 1700-1900, MIT Press, 2009. ProQuest Ebook Central, http://ebookcentral.proquest.com/lib/ucsc/detail.action?docID=3339841. Created from ucsc on 2019-07-30 19:35:03.