# data[].attributes.body
Materiali per prepararsi all'Esame di Stato 2020

# data[].attributes.attachment_link.readable_text
Percorsi CLIL
[Storia] Infos zur deutschen Geschichte nach 1945: scarica il PDF >>
[Storia] Fokus auf... die ehemalige DDR: scarica il PDF >>
[Economia] Deutschland: ein internationaler Handelspartner: scarica il PDF >>
[Arte] Expressionismus oder die Zertstörung der Formen: scarica il PDF >>

# data[].attributes.attachment_link.title
Materiali per prepararsi all'Esame di Stato 2020

# data[].attributes.attachment_link.description
Percorsi CLIL [Storia] Infos zur deutschen Geschichte nach 1945: scarica il PDF >> [Storia] Fokus auf... die ehemalige DDR: scarica il PDF >> [Economia] Deutschland: ein internationaler Handelspartner: scarica il PDF >> [Arte] Expressionismus oder die Zertstörung der Formen: scarica il PDF >>

# data[].attributes.attachment_link.readable_text
Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.

India maternal mortality rate for 2015 was 174.00, a 3.87% decline from 2014.

India maternal mortality rate for 2014 was 181.00, a 4.23% decline from 2013.

India maternal mortality rate for 2013 was 189.00, a 4.06% decline from 2012.

India maternal mortality rate for 2012 was 197.00, a 4.37% decline from 2011.

# data[].attributes.attachment_link.description
Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP.

# data[].attributes.body
WHAT'S GOING ON IN INDIA...

# data[].attributes.attachment_link.readable_text
Disattiva il Filtro Famiglia

Disattivando il Filtro Famiglia potrai trovare contenuti espliciti e per adulti tra i risultati della ricerca web, video o immagini. Cliccando su "Accetto" dichiari di avere minimo 18 anni e di accettare le Condizioni di utilizzo del servizio.

Altre info su Protezione dei minori

Per bloccare le impostazioni della Ricerca sicura, vai a Preferenze

# data[].attributes.attachment_link.title
maternal deaths history IN INDIA - Yahoo Video Search: risultati della ricerca di video

# data[].attributes.attachment_link.description
Disattivando il Filtro Famiglia potrai trovare contenuti espliciti e per adulti tra i risultati della ricerca web, video o immagini. Cliccando su "Accetto" dichiari di avere minimo 18 anni e di accettare le Condizioni di utilizzo del servizio.

# data[].attributes.body
HISTORICAL TRENDS IN MATERNAL MORTALITY RATES (HISTORY)

# data[].attributes.attachment_link.readable_text
ABSTRACT

High maternal mortality was a feature of the Western world from the mid-19th century, when reliable record keeping commenced, to the mid-1930s. During this time, maternal mortality rates tended to remain on a high plateau, although there was wide disparity between countries in the height of the plateau. From ≈1937, maternal mortality rates began to decline everywhere, and within 20 y, the intercountry differences had almost disappeared. The decline in maternal mortality rates was so dramatic that current rates for developed countries are between one-fortieth and one-fiftieth of the rates that prevailed 60 y ago. In this paper, the reasons for the high mortality before 1937 and its decline since that date are discussed. It is suggested that the main determinant of maternal mortality was the overall standard of maternal care provided by birth attendants. Poverty and associated malnutrition played little part in determining the rate of maternal mortality. This view is supported by much evidence, including the fact that, unlike for infant mortality rates, maternal mortality rates tended to be higher in the upper than in the lower social classes. The potential relevance of these findings to developing countries is discussed.

INTRODUCTION

The extent to which lessons can be learned from history (or the study of history) justified on practical or utilitarian grounds is questionable. Whether it is the history of medicine, politics, war, or anything else, it is dangerous to assume that the determinants of events in the past will operate in the same way in the present. If the butterfly of chaos theory flaps its wings in different places at different times, the results are never the same twice. Nevertheless, a review of the history of maternal mortality rates may elicit some questions and even suggest a few answers to the question of whether anemia control does, or can, reduce maternal mortality.

HISTORICAL TREND IN MATERNAL MORTALITY RATES

The trend in maternal mortality rates in England and Wales between 1880 and 1980 was such that the rate of maternal mortality remained on a high plateau through the mid-1930s, after which there was an abrupt and steep decline ( Figure 1). This pattern seemed so extraordinary that there were, initially, suspicions that the data were wrong or that an artifact had been mapped as a result of changes in the method of recording maternal deaths. After it was established that this was not the case ( 4- 6), 3 major questions arose. First, was this pattern of maternal mortality peculiar to Britain or was it similar to that seen in other countries? Second, why did maternal mortality rates remain on a high plateau, albeit with spikes and troughs, from the 1850s to the mid-1930s although overall mortality, infant mortality, and mortality due to infectious diseases had started to decline by ≈1890-1900 ( 7)? These well-known demographic transitions are confidently attributed to a general rise in the standard of living emerging from better hygiene, better housing, better nutrition, and better general health, and are not accredited to medical care. If the above is true, why had these very factors failed to reduce maternal mortality rates between 1900 and 1935? Third, what caused the abrupt change in the maternal mortality rate in the mid-1930s with the subsequent steep decline that continued at almost exactly the same rate for the next 50 y? This question is important because, unlike disease-specific mortality rates, no other mortality rates showed such a profound decline in the second half of the 20th century. The risk of women dying in childbirth in the 1920s and 1930s was still as high as it had been just after Queen Victoria came to the throne in the 1850s. Today, however, the risk of women in England and Wales dying is between 40 and 50 times lower than it was 60 y ago.

The first question can be answered by comparing maternal mortality rates in the United States, England and Wales, and Sweden ( Figure 2). In all 3 settings, maternal mortality rates tended to remain on a high plateau and at very much the same level until the end of the 19th century. In Britain as well as in France, Belgium, Australia, and New Zealand, the plateau continued with only a slight decline to a lower mortality level through the first 3 decades of the 20th century. There were, however, some striking differences between the pattern seen in the United States and in the northwestern European countries, ie, Sweden ( Figure 2), the Netherlands, and Denmark ( 6).

There are few reliable data on maternal mortality rates in the United States before 1915, but thereafter, the United States had the highest rates of maternal mortality of any developed country ( 6). The data, however, need to be interpreted with caution for 2 reasons. First, the data were based on the expanding death registration area, ie, a limited number of states only, until 1933. Second, the criteria used to define a maternal death in the United States differed from those used in Britain. In Britain, deaths that would have been excluded as indirect maternal deaths (eg, influenza in pregnancy), were included in the US published data. In 1935, a well-designed survey by Elizabeth Tandy ( 10) of the Children's Bureau showed that of the number of deaths in the United States exceeding the number in Britain, one-half were due to differences in the classification of deaths or to the methods of data collection, whereas about one-half were real.

Puerperal fever and antisepsis

To understand the trends in the rates of maternal mortality in the countries of northwestern Europe, which were consistently much lower than elsewhere, a comment is needed on puerperal fever. This fever was the most common cause of maternal mortality before the mid-1930s, accounting for ≥40% of all maternal deaths ( 6, 11).

Contrary to perceived wisdom, Semmelweis's work between 1847 and 1860 on the use of antisepsis to prevent puerperal fever had virtually no effect on deaths from puerperal fever in any country. Around 1880, Listerian antisepsis was gradually introduced into obstetrics, which greatly reduced the maternal mortality rate in maternity hospitals (6). Hospital deliveries, however, were so few that national maternal mortality rates were only reduced if antisepsis was used both extensively and properly in home deliveries.

From ≈1890 to 1900, there was a marked decline in maternal mortality rates in the countries of northwestern Europe. This decline in maternal mortality rates leveled out by ≈1910, but at a much lower level than that for Britain or the United States. Although only data for Sweden are shown in Figure 2, the same low levels occurred in Norway, Denmark, and the Netherlands, where there was a long tradition of home deliveries by well-trained and well-supervised midwives who conscientiously used antiseptic techniques from the time they were introduced. There was also a tradition of minimum surgical interference in home and hospital deliveries in northwestern Europe ( 6, 12).

Maternal mortality, home deliveries, and midwives

Historical data show that maternal mortality rates were lowest for home deliveries undertaken by trained and supervised midwives with no exceptions. Two examples from a wide range of evidence are presented below (6).

The rural nurse midwives of the Queen's Institute of Nursing, which was an organization of highly trained and supervised midwives in England and Wales, kept meticulous records on all maternal deaths occurring at home or after transfer to a hospital. This organization was particularly active between the 1920s and 1940s and achieved very low rates of maternal mortality similar to, if not better than, the rates achieved in the northwestern European countries.

Similar low levels of maternal mortality were achieved during the 1920s and 1930s in the United States by a remarkable service in the history of maternal care that was founded by Mary Breckinridge. Midwives in the Kentucky Frontier Nursing Service traveled on horseback to assist with deliveries, which were all at home in a poor rural farming community with low living standards. Despite the poverty, maternal mortality rates were ≈10 times lower than those in the nearby city of Lexington and the United States as a whole ( Table 1) ( 6).

High maternal mortality due to unnecessary interference

In contrast with the above findings, maternal mortality rates were very high in countries, states, regions, or areas where most deliveries were performed by physicians, especially in the hospital. Maternal mortality rates were also high when maximum surgical interference in normal or potentially normal labors was encouraged or advocated. A leading American obstetrician in the 1920s, Joseph Bolivar DeLee ( 13, 14), wrote a paper entitled "The prophylactic forceps operation" in which he advocated that procedures for ordinary deliveries be changed to include anesthetizing every patient in the second stage of labor, delivering the baby with forceps, and manually removing the placenta using the "shoehorn maneuver." His advice was heeded by many obstetricians and horrendous examples of iatrogenic mortality resulted. Another example, from Britain, was the widespread use of chloroform and forceps by general practitioners in uncomplicated deliveries between ≈1870 and the 1940s. This was described by one observer as a tendency a "little short of murder" ( 15) and accounted for many unnecessary deaths.

Maternal mortality and social class

Another unexpected finding related to maternal mortality, which was the basis for the second question raised at the beginning of this paper, was the inverse relation between maternal mortality rates and social class. Here the evidence comes almost entirely from Britain. Infant mortality is known to be strongly related to social class; the highest rates are found among the working classes, whereas the lowest rates are among the professionals. From at least the 1830s, however, the risk of dying in childbirth was higher in social classes I and II (the upper and professional classes, respectively) than it was in social classes IV and V (the skilled and unskilled laborers, respectively). An example of this is shown in Table 2, which gives data for 1930-1932 ( 16). The only plausible explanation for this social class difference is that the upper classes were more often delivered by physicians and, therefore, more likely to suffer unnecessary interference, whereas the lower classes were delivered by midwives, almost all of whom were trained by 1930-1932.

Causes of decline in maternal mortality after the mid-1930s

To answer the third question pertaining to the reason for the sudden decline in maternal mortality rates in the 1930s, it is necessary to look at changes in obstetric care. The initial impetus for the decline in maternal mortality rates was the introduction of sulfonamides, which were extremely effective against strains of Streptococcus pyogenes (the β-hemolytic streptococcus, Lancefield group A), which was the cause of most deaths of puerperal fever ( 6, 11) as shown in Figure 3. Other factors that contributed to the reduction in maternal mortality rates were introduced gradually. They included the use of ergometrine, blood transfusions, and penicillin; better training; better anesthesia; improved organization of obstetric services; less interference in normal labors; and the decline in virulence of the streptococcus.

The decline in maternal mortality occurred at very much the same rate throughout the developed world. In 1930, differences in maternal mortality rates between countries were large: rates ranged from 250 deaths/100000 births in the Netherlands to ≈700 deaths/100000 births in the United States (Figure 4). By 1950, the rates in all countries were similar and by 1960 they were almost the same, ≈60/100000 births. This convergence of maternal mortality rates throughout the developed world was an accomplishment that even the most optimistic obstetrician of the 1930s would not have thought to be possible within the span of 15-20 y.

The profound changes in the underlying causes of maternal mortality that accompanied the decline in maternal mortality rates in England and Wales are shown in Table 3. Puerperal fever went from being the main cause of maternal deaths in the 1870s to being a subordinate cause by the 1970s. Hypertensive disease (previously referred to as toxemia and eclampsia, or puerperal convulsions) moved from being the third most prevalent cause of maternal deaths in the 1870s to the number one cause in the 1970s, and deaths from hemorrhage (antepartum and postpartum) moved from being the second most prevalent cause in the 1870s to the sixth most prevalent cause in the 1970s. Although there may be differences in detail, it is probably safe to say that in developing countries with high rates of maternal mortality today, the rank order of causes is reasonably similar to that in Britain in the 1870s. If this is the case ( 4- 6), it is a matter of great significance because it suggests that measures taken to reduce maternal mortality in the developed world ≈50 y ago may be the measures that would work best today in countries or regions with high rates of maternal mortality.

The profound decline in maternal mortality rates since World War II has been completely dependent on accurate data for both the number and causes of maternal deaths. In the late 1940s, which were the early years of the National Heath Service, the British Ministry of Health introduced the system of confidential inquiries into the recording of maternal deaths, and every maternal death was subject to the most intense, but confidential, inquiry. It was perhaps the first official example of what has become known as a clinical audit. It is impossible to know what would have happened without this system of continuous audit, but the reports certainly give the impression that they identified the avoidable maternal deaths and led to ways of preventing such deaths.

NUTRITION AND MATERNAL MORTALITY

The significance of the features described in the above section suggest that there were 3 main determinants, but not necessarily causes, of maternal mortality in the past: the social and economic status of birthing women, the standard of obstetric care, and the virulence of streptococcus. Streptococcal virulence is an immensely complex subject that is not directly related to the topic of anemia and maternal mortality and will not be discussed here.

It is not known whether maternal mortality rates were high in the past primarily because the health of women was impaired, especially in terms of nutrition and anemia, or because of the standard of maternal care. It does not appear that there were any surveys on anemia in populations of expectant mothers before the 1930s and, even if there were, the accuracy of the measurements would be questionable because of the assessment techniques available at that time. There are, however, copious data on maternal mortality rates in poverty-stricken populations, in whom it is reasonable to assume that malnutrition was prevalent, and some physicians believed malnutrition was the underlying cause of high maternal mortality. For example, a medical officer of health in the 1930s said, with a touch of exasperation, that what was needed in South Wales (an area well known for having high maternal mortality) was a herd of cows and not a herd of obstetricians (19). Although a memorable remark, it is likely that he was wrong.

There is overwhelming evidence that social and economic conditions were very weak determinants of the levels of maternal mortality, whereas the standard of obstetric care was a very strong determinant. In situations in which nutritional status is so low that it approaches starvation, however, this may not be true. Under the conditions of poverty and associated malnutrition that were seen in parts of developed countries in the 19th and first half of the 20th century, it was care at parturition and not malnutrition and other concomitants of poverty that determined the level of maternal mortality. Although it can be argued that social status and standards of maternal care were not wholly independent variables, because the rich could buy what they thought was the best maternal care and the poor could not, the reduction in maternal mortality in the past was generally independent of the economic status of the mothers. This is exemplified in the following 2 cases.

The industrial town of Rochdale in northwestern England had, in the early 1930s, the unenviable distinction of having the highest rate of maternal mortality in the country. Most of the population was poor, deprived, and malnourished. Rochdale also had an appalling standard of maternal care. In 1930, an exceptionally vigorous medical officer of health was appointed who totally reformed maternal care in Rochdale. Although there was no change in social conditions, maternal mortality rates decreased within a few years from 900/100000 births (compared with 400-500 maternal deaths in England as a whole) to 170 maternal deaths and this low rate was sustained (15).

The second example comes from the United States, where a religious group in the state of Indiana, called the Faith Assembly, was investigated in the early 1980s. This sect consisted of well-nourished, middle-class, white citizens who had ordinary middle-class jobs. Their religion, however, led them to reject all forms of orthodox medicine, including the services of obstetricians and midwives. Maternal care consisted of prayer and delivery by family or friends. Maternal mortality in this group was 872/100000 births compared with 9/100000 for Indiana as a whole, a rate that was 92 times higher (95% CI: 19, 280) than that for the remainder of Indiana (20).

High and low rates of maternal mortality have never corresponded to changes in economic conditions ( 6). Most notably, US maternal mortality rates declined steadily from the beginning of-and throughout-the great depression of the 1930s ( Figure 2).

CONCLUSIONS

In developed countries until the mid-1930s, maternal mortality rates were high. The major determinants of the high levels of maternal mortality were the standard of care at delivery and the virulence of Streptococcus pyogenes, which caused almost all deaths from puerperal fever. It was certainly true that before 1937-and probably thereafter-malnutrition associated with poverty was a surprisingly minor determinant of levels of maternal mortality. High maternal mortality was substantially reduced only by providing high-quality maternal care by the standards of the time, not by improving the diet.

The sudden and dramatic decline in maternal mortality rates, which occurred after 1937, took place in all developed countries and eliminated the previously wide country-level differences in national mortality rates. The main factors that led to this decline seem to have been successive improvements in maternal care rather than higher standards of living. As a result of this decline in maternal mortality in developed countries, there is now no mortality for which there is a greater disparity between the developed and the developing world than the disparity in maternal mortality rates.

Interventions to reduce maternal mortality rates are likely to be much more effective if the underlying causes are known. This is particularly true when the underlying causes vary in extent, importance, or both, among developing countries. For this reason and despite the practical difficulties in doing so, it is important to assess attributable risk of maternal mortality in developing countries. In other words, it is important to identify the causes of maternal mortality whenever possible and to try to estimate the degree of certainty in the data.

DISCUSSION

Dr Ladipo: I was very impressed by your graph on maternal mortality from Britain and one Nordic country. One thing that struck me was that most of the natural decline in maternal mortality was after 1940, which coincided with the introduction of the National Health Service, when every individual in the United Kingdom had access to good care. In addition, in a welfare state, people may have been disadvantaged or unemployed, but the fact that they had reasonable income from the government meant that at least their level of nutrition would be within acceptable limits. Other factors that were more important for the sharp decline in maternal mortality were the use of antibiotics, availability of blood transfusion, and reduction in the high-risk pregnancies in the United Kingdom-notably teenage pregnancy and grand multiparity. Contraceptive use and acceptance by the wider population increased over time, particularly after the 1930s, and the role of contraception in that graphic change should not be overlooked in the discussion. The availability of contraception and improved health care services that were due to the National Health Service improved blood transfusion services and access to operative delivery and may have made a contribution to that reduction.

Dr Loudon: The beginning of the sudden improvement in maternal mortality was in 1937 ( Figure 1). In the first few years, it was almost entirely due to the introduction of sulfonamides and later penicillin, and this applied to other countries as well. If the decline is plotted on a logarithmic scale, you get a straight line. Richard Doll (personal communication, 1993) remarked that this showed that the decline was not due to one factor but to many. The factors that may have been responsible were, first, sulfonamide and penicillin for infection; second, ergometrine use to reduce postpartum hemorrhage; third, universal blood transfusion; and fourth, much better cooperation and better medical education and so on.

What is interesting was that although the decline was partly due to the National Health Service, the fall occurred at the same rate in almost every country in the world. Figure 4 shows a huge disparity in maternal mortality rates among the United States, Australia, England and Wales, and the Netherlands before 1937, but after that it converges. By 1946, maternal mortality in these countries was separated by a whisker. There were ≈60 deaths for every 100000 births and every country congratulated itself on its obstetric services. The United States said "this was a triumph of capitalism and modern medicine." Australia said, "We Australians have always been the best." Britain said, "It is the National Health Service." Sweden and the Netherlands said, "It is our marvelous service." Maybe each was right, but you can produce your own explanation. The disparity disappears almost completely by 1960 and has continued to today, where only a hairbreadth's difference in maternal mortality rates exists among these countries.

Dr Ladipo: You talked about the pattern from 1850 onwards. What happened before 1850?

Dr Loudon: The important work was by Wrigley and Schofield, who wrote a population history of England [Wrigley EA, Schofield RS. The population history of England. A reconstruction. Cambridge: Cambridge University Press, 1981]. They found maternal mortality rates were certainly higher at ≈400-500 per 100000 births throughout the 19th century. It was a bit higher at the beginning of the 19th century and was up to perhaps 1000 per 100000 births in the early part of the 18th century. I have a graph in my book [Loudon I. Death in childbirth. Oxford: Clarendon Press, 1992] that shows maternal mortality stretching back in history and, as you go back, it goes up very slightly and then we lose track because there really are no data as yet. Nevertheless, a dramatic fall in maternal mortality did not occur until 1937. A more dramatic fall was observed for Sweden, but that is another issue.

There is one recent and absolutely fascinating fact that does not affect maternal mortality at all, but it affects the topic we are talking about today. If you trace back infant mortality divided into neonatal and postneonatal mortality right through the 19th century, postneonatal mortality rates were much higher than neonatal rates, and that continued into the 20th century. Then, both mortality rates declined, but the neonatal rate went down more slowly than the postneonatal rate, as expected. The crossover point was ≈1950, after which neonatal mortality was higher than postneonatal. It has recently been discovered that for reasons that none of us can fathom, between 1700 and 1750 neonatal mortality was higher than postneonatal mortality. After this time, both mortality rates suddenly came down, crossed over, and adopted the position described above for the next 150 y. This is purely a point of academic fascination, discovered by Professor Wrigley. I do not know the explanation, and we have discussed it back and forth. To come back to your original question, there was a slight fall in maternal mortality rates through the 18th century as far as we know. Indirect work, including Wrigley and Schofield's brilliant work on parish records, has confirmed that there was not much of a fall in maternal mortality rates then in the United Kingdom. We do not know what happened in other countries except in Sweden where there was a profound fall.

Dr Maine: Are there situations in maternal mortality where each attributable and relative risk might have advantages?

Dr Loudon: As far as there is any analogy-and a very slight analogy may exist between historical work and modern work in the developing world-my feeling is that trying to get the best figures you can on attributable risk is essential, but you must recognize that it is an extremely difficult thing to do. You have to try to estimate the range of error in the data that you get, but without at least trying to do so I think you are hamstrung in trying to intervene.

REFERENCES

# data[].attributes.attachment_link.title
Maternal mortality in the past and its relevance to developing countries today

# data[].attributes.attachment_link.description
High maternal mortality was a feature of the Western world from the mid-19th century, when reliable record keeping commenced, to the mid-1930s. During this time, maternal mortality rates tended to remain on a high plateau, although there was wide disparity between countries in the height of the plateau.

# data[].attributes.body
HOW TO CREATE DIAGRAMS AND COLLECT DATA (MATHS)

# data[].attributes.attachment_link.readable_text
Background

In this section, we focus on the use of data from full birth histories (FBH) or truncated birth histories (TBH). The key characteristics of such data are that for each birth included, the date of birth, survival status and (if dead) age at death are recorded. Analysis of the data typically uses life table approaches. Indirect estimation of child mortality, and estimation of child mortality from survival of a recent birth, are covered in the section on indirect estimation of child mortality and the section about the preceding birth technique.

Data requirements and assumptions

Data required

For each woman of reproductive age (in some settings for cultural reasons information collection is limited to ever-married women):

the name of each child born alive;

the month and year of birth of each child;

the child's sex (optional);

Whether the child is still alive; and

if the child has died, the age at death (age at death in the DHS program is collected in days for deaths that occur in the first 28 days of life, in months for deaths at ages one to 23 months, and in years thereafter).

Important assumptions

Children still alive and children dead are reported with similar accuracy.

Dates of birth and ages at death are reported with reasonable accuracy.

No correlation exists between mortality risks of children and survival rates of mothers (whether as a result of mortality or migration) in the population.

Caveats and warnings

The dangers associated with working with directly-collected data arise from two sources. The first is the risk of survivor bias as only living mothers are asked the detailed birth histories used to generate the data. In situations where it is anticipated that deceased mothers might have had different fertility, or different mortality among their children, from surviving mothers, there is a risk of appreciable bias in the estimates derived. Aspects of survivor bias are discussed in the section on the introduction to child mortality analysis the section on the effects of HIV on child mortality estimation.

The second danger is that if an upper age limit is applied to the women from whom detailed birth history data are collected, truncation bias becomes more significant the further back in time one looks. If an age limit of 49 is applied to the collection of the data, this means that for the period 10 years before the survey, information is only available for women who were then aged up to age 39. Hence, child mortality estimated from such data for earlier time periods will be increasingly based on the experience of younger women. In turn, this might lead to measurement bias, as this truncation results in an over-representation of first births among younger women, meaning that child mortality thus estimated is likely to be increasingly overestimated for earlier time periods. There is some evidence that such over-estimation is counter-balanced by underestimation arising from recall bias (and selective omission of children who have died in periods longer in the past).

Data evaluation and data analysis

Regardless of how data have been collected, or of one's knowledge of how thoroughly interviewers were trained and supervised, careful review of data quality is an essential first step of any analysis. All data sets contain errors. These can result from many sources, such as an interviewer cutting corners or an interviewee simply not knowing the correct answer to a question. Each section below starts with a description of data evaluation techniques, before progressing to analysis methods. These evaluation techniques examine both internal consistency within a data set, and external consistency with other data sets for the same population. It should be noted in passing that the presence of data errors does not necessarily mean that a data set should not be analyzed; the important thing is to know how large the errors are, and take them into account when interpreting the findings.

The full birth history: data quality assessment

The first step in a thorough data quality assessment is to examine the extent of missing values. In an FBH, values may be missing for a number of reasons. For example, whole households included in the original sampling frame may be missing. Further, eligible women within interviewed households may have no data because the woman could not be interviewed. In addition, individual items within an FBH may be missing because the interviewed woman did not know a child's birth date, or whether a child was still alive, or (if the child had died) the age at death. The proportions of events potentially affected by these errors need to be examined. Missing items may be imputed during data cleaning, but imputed values should be flagged. The absence of missing values should not be taken as strong evidence of data quality, and may in fact be taken as a warning flag: in some surveys, interviewers and supervisors are trained to avoid missing values, and in such cases data may be more or less made up by the interviewer.

The second step in the data quality assessment is to examine the aggregate results for implausible irregularities. The irregularities most often identified are in sex ratios at birth, in annual distributions of live births, and in ages at death. In the absence of intervention, sex ratios in human populations are generally in the range of 100 to 106 males per 100 females. Sex ratios for birth cohorts outside this range are probably indicative of error. Sex ratios that increase for cohorts born a longer time before the survey are particularly clear indicators of an error, in this case under-reporting of female births that occurred in the distant past.

In the absence of major positive or negative events, births will normally be fairly smoothly distributed by calendar year (in that while seasonality is common, this should not affect the annual numbers. Possible errors can be identified by calculating 'birth ratios', defined as

where is the number of births reported in a given year, t.

An error commonly found in DHS data sets has come to be called "birth transference". DHS surveys collect a substantial amount of additional data about children born since some cut-off date, usually 1 January of the calendar year five years before the survey. It is often the case that births that occurred in that year are reported as occurring in the previous year, presumably to reduce work load. This results in a deficit of births in the year following the cut-off, and a surplus in the year immediately before the cut-off. Birth ratios will highlight this error, since the birth ratio for the year starting with the cut-off will be low, and that for the preceding year will be high. Very often, this birth transference is greater for children who have died than for those who are still alive, so it is good practice to calculate separate ratios for surviving and dead children.

Irregularities in reporting ages at death can similarly be identified by calculating ratios of deaths at some age x to the average number of deaths at ages ( x-1) and ( x+1). In DHS data sets, there is generally an excess of deaths at age 7 days, to a lesser extent at age 14 days, and at age 12 months.

DHS conveniently publishes these data quality indicators at aggregate (national) level in survey reports (often in Appendix C). Analysts wishing to carry out sub-national analyses will need to calculate indicators themselves.

The data quality indicators described above measure internal plausibility. However, data can be internally plausible and still wrong. Data should also be evaluated by comparison with other surveys for the same population. Cohort comparisons are particularly powerful, for example comparing the average number of children ever borne by women aged 30-34 reported in one survey to the average number borne by women aged 35-39 reported in another survey five years later. Similar comparisons can be made of average numbers of children dead. Sequences of births by single calendar year for overlapping periods can also be compared, though one has to bear in mind that births in the past are increasingly truncated in birth histories limited to women aged 15-49 at the time of the survey.

The full birth history: Calculation of child mortality indicators for birth cohorts

Widely used indicators of child mortality are expressed as probabilities. Thus the Infant Mortality Rate (IMR) is (approximately - as conventionally defined, the IMR is infant deaths in a year divided by births in the year, a value which closely approximates ₁ q ₀) the probability of dying by exact age 1, ₀, and the Under-five Mortality Rate (U5MR) is the probability of dying by age 5, ₅ q ₀. Strictly speaking, probabilities are real cohort measures, even though most life tables calculate synthetic cohort measures for specified time periods from age-period mortality rates. Calculating cohort probabilities from FBH data is very straightforward. For example, the cohort IMR for births in the 12 to 23 months before the survey is simply the number of such births that died before the age of 1 divided by the number of births. Similarly, the cohort U5MR for births 5 to 9 years before the survey is the number of such births reported to have died before exact age 5 divided by the number of such births. Figure 1 shows the Lexis diagram representation of the age-cohort probability of dying by age 1 for the cohort born in July 2001 (in green), and the age-period mortality of 5-month olds in calendar year 2002 (the blue rectangle, relating to the example used later in this section).

Table 1 shows the relevant numbers and calculations for age-cohort probability of dying by age 1 for the cohort born in the 12-23 months before the survey and for the probability of dying by age 5 for the cohort born in the 5 to 9 years before the survey, using data from the 2004 Malawi DHS. Note that there is no period interpretation of such cohort values; in the U5MR example, the cohort probability reflects mortality risks in every one of the 10 years before the survey. Also note that the probability of dying by age x can only be calculated for cohorts that were born at least x years before the survey. Both these considerations limit the value of the cohort measures, since for most purposes analysts and policy-makers are more interested in time period measures.

Table 1: Calculation of IMR and U5MR for cohorts: Malawi 2004 DHS

The full birth history: Calculation of child mortality indicators for time periods

Period-specific measures are estimated using the synthetic cohort concept. Mortality rates for narrow age ranges and defined calendar periods are calculated on the basis of events and exposure in these rectangles in the Lexis Diagram. The rates are then converted into implied probabilities, using standard demographic relations (see, for example, Preston, Heuveline and Guillot (2001)) and making generally mild assumptions about the distribution of deaths in each rectangle. Finally, the probabilities of dying are applied successively to an initial hypothetical cohort of births to compute a survivorship curve ℓ( x) for each age x, from which it is easy to derive probabilities of dying.

FBH data lend themselves to these life table calculations quite easily. If data are collected following the standard DHS practice - as month and year of birth and age at death in days, months or years, depending on the age - deaths can be located with little ambiguity in age-period rectangles of the Lexis Diagram. (There will be some residual ambiguity, because of the imprecision of the information on date of birth and age at death, but the impact will depend on the sizes of the rectangles.) Here we describe an approach based on the calculation of age-specific mortality rates for a single calendar year (age-period rates) for mortality up to age 5. Extension to other time periods is straightforward. It is assumed that data are in standard DHS format, that is, birth dates are recorded in century month (CMC) format, and ages at death in days, months or years. Unit record data must be available. The unit of age used is the month. The basic calculations are therefore of age-specific mortality rates by month of age and calendar year. These rates are converted into corresponding probabilities of dying in each month. These probabilities are then converted into probabilities of surviving, and are chained together over whatever age range is required (typically up to age 5). The key to the calculation is to assign deaths and exposure time to one-month age segments across a calendar year.

Data manipulation

Four variables in a DHS birth data set are required:

1. b3, date of birth in CMC;

2. b5, whether child is still alive;

3. b6, age at death, where the first digit represents the unit (1 indicating days; 2, months; and 3, years) and the second and third digits represent the value given that unit; and

4. v005, sample weight, expressed in millions.

Note that variable b7, age at death (months-imputed) is not used. This variable does not lend itself to the mortality rate approach described here, because in cases in which age at death is recorded in years, the 'imputed' month is actually the lower bound of the age interval; that it, if age at death is recorded as '3 years', the imputed age at death in months is recorded as 36 months. Using this variable will result in systematic mis-location of deaths in time.

Application of method

Step 1: Manipulation of age at death and calculation of estimated birth date and age at death

We want to locate deaths in a calendar month of occurrence. Since we do not have a precise date of birth (only CMC), and in general we do not have a precise age at death (except for neonatal deaths), we need to impute both a date of birth and an age at death. We can perform this imputation using random numbers.

It is evidently undesirable - for reasons of lack of reproducibility, amongst others - to make use of a true random number generator to produce the random numbers referred to above. In addition, 'true' randomization risks creating a spurious impression of precision. As an alternative, we propose creating pseudo-random numbers from variables that are routinely available in DHS data and that can be applied in the algorithm above. It is an easy matter to create new variables apportioning the records into deciles based on the reported day of interview ( v016 in a DHS) and household number ( v002). (These variables have been chosen on the grounds that there is unlikely to be any correlation between them and child mortality). These new variables will take the values in the range (0, 1 ... 9). Dividing each by 10, and adding 0.05 results in two new uniformly distributed variables, random1 and random2, taking values in the range (0.05, 0.15, ... , 0.95).

It is then straightforward to impute a date of birth ( dob, in months) if births in the month of interview are excluded from analysis by adding random1 to b3 (the CMC of the child's date of birth). The method for imputing an age at death (in units of months) depends on the 'unit'. For 'unit' = 1 (i.e. age at death measured in days), age at death ( aad) can be estimated as ('value'+ random2)/31 (for age at death in days this is not necessary, but is described for symmetry); for 'unit' = 2, age at death is 'value' + random2; and for 'unit' = 3, age at death is ('value' + random2)*12.

Step 2: Location of deaths in target year

For each month-of-age mortality rate, the events consist of deaths at that age in the period of investigation. Step 1 has imputed age at death in months. The date of death dod is given by the sum of imputed month of birth dob and imputed age of death aad. If imputed age at death is within the age range and the imputed date of death falls within the period of investigation, we have a relevant event.

Step 3: Derivation of exposure to risk

The calculation of exposure to risk is intricate, but relatively straightforward. The age range of the investigation refers to those ages (defined in appropriate units) for which we want to measure mortality. We define the lower bound of the age range to be , and the upper bound to be .

The period of investigation is the measure of the time period for which we seek to estimate mortality, and is defined as the period (- ), where is the end date of the period of investigation, and the start date, measured in the same units as that defined by the age range.

Graphically, then, we seek to measure mortality in the age and period defined by the heavy lines in Figure 2.

An individual's life course by age and period is represented by the diagonal lines (as with a conventional Lexis diagram). Five possible scenarios (labelled (a) through (e)) are portrayed. Any individual's position in the space can be defined by their age at , . It follows, further, that any person aged x at , if she or he does not die before , would be aged = +(- ) at time . We define the age at death of those deaths that occur in the specified age range in the period of investigation to be . The relative contribution of each scenario to the exposure to risk is determined by the algorithms in Table 2.

Table 2: Algorithm for determining exposure to risk

Applying these rules to define the exposure in the age range in the period of investigation for each individual and aggregating gives the total exposure to risk, which is the denominator for the mortality rate. Summing the deaths occurring in the age range in the period of investigation provides the numerator.

Step 4: Weighting and cumulating events and exposure time

The sample weight variable in a standard DHS recode file is v005. This variable has a mean of 1,000,000. To avoid the appearance of huge sample sizes (and much too narrow confidence intervals) it is recommended first to recalculate the weight as (v005/1,000,000). Let us call this new variable wgt. Mortality rates can be calculated by considering the contributions of each of the N children in the survey to the number of events and the total exposure time. The age-specific mortality rate age x to x + 1 (in months) in a period, j, is

where M( x,j) is the age specific rate for age x and year j, D( i,x,j) is a binary variable indicating the death of child i at age x in year j (1 if the death occurs, 0 otherwise), E( i,x,j) is the exposure time of child i at age x in year j, and wgt( i) is the sample weight (mean 1.0) of child i.

Step 5: Calculating probabilities of dying from age-specific mortality rates

The rates calculated in Step 4 are per month of age exposure. It is therefore necessary to adapt the standard formula for deriving a period probability of dying from a rate to take this into account. Given that we have made a number of simplifying assumptions and are working with narrow age ranges, it is adequate to assume that deaths are evenly distributed across each single month age range. We can then calculate q( x) as

Survivorship probabilities from birth to any age can then be obtained by chaining together survivorship by month (i.e. (1- q( x, j)) ) terms. Thus for instance

Worked example

As noted above, direct estimation of child mortality from a birth history requires working with unit record data rather than tabulations. As a worked example, we will therefore illustrate with a limited number of records adapted from a DHS, specifically the mortality of 5-month olds in 2002 from the 2004 Malawi DHS. Only children born between 1 July 2001 and 31 July 2002 are at risk of dying at age 5 months in calendar year 2002 (children born before 1 July 2001 would be aged 6 months or more by the beginning of calendar year 2002, and those born after 31 July 2002 would not have reached age 5 months in the year). Only relevant records are shown, that is, those for births between month 1218 and 1230 in CMC terms (July 2001 to July 2002). In practice, we would also exclude any births that died before five months of age, but we will include them in the example to show that we exclude them from calculations.

Table 3 shows the key variables for 50 records from the 2004 Malawi DHS; note that these are birth records, not woman records.

Table 3: Basic birth history data for direct estimation of child mortality

Step 1: Manipulation of age at death and calculation of estimated birth date and age at death

Random numbers random1 and random2 are derived as described above, resulting in revised values of dates of birth and age at death, dob' and aad'. The date of death dod' is estimated as the sum of the imputed month of birth dob' and imputed month of death aad'. Column 10 of Table 4 shows dod'.

Table 4: Derivation of imputed date of birth, age at death and date of death, Malawi, 2004 DHS (50 cases)

Step 2: Location of deaths in target year

A relevant death in terms of period is one with a CMC between 1224 to 1235. The deaths in records 3, 6, 31 and 41 of Table 4 are therefore not relevant because they are deemed not to have occurred in 2002. The deaths in records 11 and 45 are not relevant because the child died at 2 months (11) or 1 month (45) of age, and therefore was not exposed to the risk of dying at age 5 months.

Step 3: Derivation of exposure to risk

Table 5 presents the calculation of the exposure to risk for the 50 cases described above. The rule used to determine the exposure is presented in the column headed 'Scenario'. The resulting exposure is presented in the following two columns for those who survive the period of investigation and those that die during the period.

For children who survive to age 6 months, those born in months 1219 to 1229 contribute a full month of exposure time to the age-period of interest (i.e. from exactly 5 to exactly 6 months). Thus record 1 (born 1223.55) contributes a full month. A child born in month 1218 will contribute (dob - 1218) months, so record 23 (born 1218.85) contributes 0.85 of a month; and a child born in month 1230 will contribute (1231 - dob) months, so record 7 contributes 1231 - 1230.55 = 0.45 months. The children born in months 1219 to 1229 who die at age 5 months will contribute (aad - 5) months of exposure; thus the death in record 14 occurs at 5.25 months and contributes 0.25 months of exposure.

Table 5: Derivation of exposure to risk for estimation of child mortality, Malawi, 2004 DHS (50 cases)

Step 4: Weighting and cumulating events and exposure time

The final step before calculating the death rate is to take account of the record sample weight in both the deaths and the exposure time, and then sum the weighted deaths and exposure. Columns 6 and 7 of Table 5 show the exposure to risk for survivors and relevant deaths. Columns 8 and 9 then multiply columns 6 and 7 respectively by the sample weight v005/1,000,000. The age-specific mortality rate M(5,2002) is then calculated by dividing the sum of the weighted deaths by the sum of the weighted exposure time:

Step 5: Calculating probabilities of dying from age-specific mortality rates

The rates calculated in Step 4 are per month of exposure. It is therefore necessary to adapt the standard formula for deriving a period probability of dying from a rate. Given that we have made a number of simplifying assumptions and are working with narrow age ranges, it is adequate to assume that deaths are evenly distributed across each single month age range, even for the first month of life. We can then calculate q( x) as

Once all the q( x,j)s have been calculated, they can be converted into their complements, probabilities of surviving, and chained together to produce survivorship probabilities and probabilities of dying from birth to any desired age.

To obtain rates and probabilities for periods longer than a single calendar year, the weighted sums obtained in Step 4 are summed across years as required. Step 5 remains exactly the same.

Note that the procedure described here differs from that used by DHS. The DHS approach calculates probabilities directly for quasi-cohorts ( Rutstein and Rojas 2003). Calculations are made for eight age groups: neonatal, 1-2 months, 3-5 months, 6-11 months, and years from age 1 to age 4. For each age range, period deaths are derived from date of birth and age at death. The risk set is an approximation of the number of children who enter that age range during the period. This approximation is the sum of all children who enter the age range and leave the age range (or would do so if they survived) during the time period, plus half of those who enter the age range during the period but would leave it after the period, plus half of those who enter the age range before the period but would leave it during the period.

Whichever procedure is used, individual-level data from the FBH will be required. Although the calculations could be carried out from detailed tables, it would be very tedious to do so. Use of a suitable computer routine is strongly recommended.

Interpretation

The key characteristic of direct child mortality estimation, namely that information is provided by surviving women who still live in surveyed households, needs to be borne in mind when interpreting results as there is risk of respondent selection bias. In particular, the mortality experience of children born in a community whose mothers no longer live in the community will not be included in the measures. If such children have higher mortality than those born to mothers who do still live in the community, mortality will be under-estimated. The most severe form of this bias is likely to result from substantial levels of HIV prevalence in the community, since such prevalence in the absence of widespread antiretroviral therapy will result in a strong positive correlation between survival of child and survival of mother (see effects of HIV/AIDS on child mortality estimation). However, some positive correlation between mother and child survival is almost certain in any population. Other reasons for bias may exist. For example, high in-migration rates will result in women reporting on the survival of children born and raised elsewhere, while high out-migration will remove responses about children who were born and raised in the community. Although it is impossible to know a priori the direction or magnitude of such biases, the analyst needs to keep in mind their potential effect. Non-response may also be an issue if women absent from the community for an extended period cannot be interviewed in person, but may have experienced different risks to their children, or may not be present in part because their children have experienced different risks.

Extension to the method: Truncated birth histories

Truncated birth history: Data quality assessment

The truncated birth history (TBH) provides fewer opportunities for data quality checks than the full birth history (FBH) because the time series of events reported is by definition truncated. If the truncation is by time period, the events reported should be representative of the time period covered, whereas if the truncation is by number of events, the events reported may be representative only of all events in quite a short period prior to the survey, and this will complicate any assessment of the sequence of events in time.

As with the full birth history, the first step should be to examine the data for missing values. The second step should involve the examination of sex ratios at birth and heaping on ages at death.

No direct assessment of birth transference will be possible, because no detailed information about dates of births is available prior to the truncation point. However, an indirect assessment is possible. A TBH should always involve the initial collection of a summary birth history. The births and child deaths for an age group of women defined as at the survey date can therefore be calculated both at the time of the survey (from the summary birth history) and (only approximately for the deaths) at the truncation point, by subtracting the births and child deaths reported in the TBH. The calculation for births is precise, but for child deaths is approximate because some of the child deaths reported in the summary birth history (SBH) will have occurred during the post-truncation period to children born before the truncation point; typically, however, the number of such extra deaths will be small given that child mortality risks drop rapidly with age of child. The data quality assessment is therefore the comparison of the proportion dead (by age group of mother at the time of the survey) of the children born after the cut-off date to that of the children born before the cut-off date.

There are two reasons why the former proportion will generally be smaller than the latter. First, the children will have been exposed to the risk of dying for a shorter period. Second, if child mortality is falling over time, they will have been exposed to lower age-specific risks as well. However, if children who have died are systematically omitted from the post-truncation period, or if they are reported in the summary birth history but not reported as having been born in the period, the ratio of the two will be inflated by data error. We can estimate a plausible error-free ratio if data are available from a full birth history for the same population at an earlier or later date. Table 6 shows data from Mongolia: three Reproductive Health Surveys, one in 1998 that included a full birth history and two - one in 2003 and one in 2008 - that collected only TBHs. The 1998 full birth history data are used to calculate proportions dead for children born before and after a comparably-defined cut-off date, and compared to the proportions calculated from the 2003 and 2008 TBH data. As can be seen, the TBH ratios are several times larger than the full birth history ratios, providing compelling evidence of transference of dead children out of the post-truncation period. In the absence of a country-specific baseline, such as that provided here by the 1998 RHS survey, ratios of 3 or higher should be taken as evidence of probable omission of dead children from the recent reference period.

Table 6: Proportions of children dead by whether the birth occurred before or during the TBH date window, Mongolia, 1998, 2003 and 2008 RHS

The truncated birth history: Calculation of child mortality indicators for cohorts

The calculation of cohort probabilities of dying from a TBH follows the same principle as that followed with a FBH: the probability of dying by age x is calculated as the number of dead children to the number of children ever born in some defined cohort born no less than x years before the survey. There is an important difference, however, as made clear in the Lexis Diagram in Figure 1, namely that the value of x is constrained by the truncation date. For example, if the truncation date is 5 years before the survey, no birth cohort will have been fully exposed to the full risk of dying by age 5, and the cohorts exposed fully to risks up to age 2 are limited to births 2, 3 and 4 years before the survey. Thus there are limits to the range of ages for which mortality indicators can be derived.

The truncated birth history: Calculation of child mortality indicators for time periods

The basic approach to calculating standard indicators from a TBH follows the same principles as that used for a full birth history: to calculate age-specific rates for a specified time period, convert them into estimates of probabilities of dying in successive age intervals, and apply the probabilities to a synthetic cohort of births to create the life table. The problem with analyzing a TBH in this way is the same, however, as that faced in calculating cohort indicators, namely that cases and exposure time become progressively more restricted as age increases. Thus if the cut-off point is five years before the survey, the measures for ages 3 and 4 will be based on small numbers and have wide sampling errors.

References

Preston SH, P Heuveline and M Guillot. 2001. Demography: Measuring and Modelling Population Processes. Oxford: Blackwell.

Rutstein S and G Rojas. 2003. Guide to DHS Statistics.Calverton, MD: ORC Macro.

# data[].attributes.attachment_link.title
Direct estimation of child mortality from birth histories

# data[].attributes.attachment_link.description
In this section, we focus on the use of data from full birth histories (FBH) or truncated birth histories (TBH). The key characteristics of such data are that for each birth included, the date of birth, survival status and (if dead) age at death are recorded.

# data[].attributes.body
ANALYSING THE PHENOMENON FROM A SCIENTIFIC POINT OF VIEW. (SCIENCE)