New approaches to the collection and analysis of data on mortality and cause-of-death

As living standards improve, mortality declines, and the age-pattern of human mortality changes. Yet, there is no one pattern of mortality decline., and different populations may have a different age structuring of mortality, even though they have a similar average level of mortality.
In the present paper we consider the dimensionality of mortality using a random sample of 109 population tables (male and female) drawn from the Human Mortality Database. Factor analysis of log( p(x))
values shows a three factor solution to be adequate, the three factors being:
1. Mortality to age 50 for men, to age 80 for women
2. Mortality for ages 50 to 80 for men
3. Mortality above age 80 for men and for women.
We compare these factor analytic measures with measures proposed in the mortality literature as measures of the shape of the mortality / survivorship curves and find that most of these reproduce necessary changes in the shape of the life-table functions as mortality declines, but do not distinguish between the different shapes of curves in life tables with similar overall levels of mortality. Following the logic of the factor analytic model, we thus suggest that the different elements of the life table are better expressed by age-specific survival probabilities, focussing on younger ages, middle ages (for men) and old ages

Maternal mortality remained as one of the indicators of health and development goal. Many countries lack the estimate of maternal mortality, and in some countries, estimates are not very reliable. In case of India, Sample Registration System (SRS) estimates of maternal mortality based on three year pooled data are considered reliable in comparison to other sample survey. It may be noted that, in many sample surveys, rare events are not studies as it required large sample size. However, if the operation of house-listing can be used meaningfully to identify a rare event like maternal mortality and there is a high probability that all the members of household will remember this event. It may be noted that, around 5 to 20 percent of project expenditure is devoted for this operation, and this operation should be utilized to identify maternal deaths. Number of deaths covered during house-listing operation of any large scale survey will be certainly more than SRS. This will also help to get reliable estimates at the state level especially for those high focused states, where immediate attention is required for maternal care programme in India. The causes of deaths obtained from the nationally representative sample will be much more useful than localized oral autopsy survey.

Self-reported morbidity i.e. measure of incidence of disease based on reported sickness has an element of subjectivity. Unlike morbidity, mortality is an objective measure based on “external assessment of health”. In India, there are evidences of regions where the reported morbidity is higher, yet the mortality is low and vice versa. Motivated from this opposing observation in morbidity and mortality and from the fact that there is a dearth of literature on exploring the relation between morbidity and mortality in Indian context, this paper studies the difference between morbidity and mortality for Indian States. The study conceptualizes Mortality–Morbidity–Gap Index (MMGI) and justifies the measure through an axiomatic characterization. An empirical illustration is carried out ranking the States of India as per MMGI using data from National Sample Survey. The study also investigates the determinants of MMGI by considering State’s income, education, urbanization, health infrastructure, and policy variables.

Mortality occurring during the first year of life is sensitive to the living conditions and health population. In Argentina, infant mortality (IM) has declined during the twentieth century but it varies considerably by geographic region. In that sense, the reality of the northwestern provinces of Argentina, a region postponed, hides in a country-level. Some authors (2009) claim that the Northern is the poorest region, and they also point that this has not undergone a significant change in recent decades (Bolsi, Longhi, & Paolasso, 2009).
We aim to perform spatial analysis of IM at a county level of that region during 2005 and 2010 detecting the existence of high risk areas which is essential to focus public policies. We use official information on vital statistics of Argentina calculated according to the causes of death, age and sex; we process data in free GIS software. Given spatial analysis of health conditions, can be a valuable tool in assessing the impact of social structures and processes, in determining health events (Barcellos, 2003), what we propose would be a major contribution to targeting and strengthening public policies aimed at reducing IM.

The rate of decline in child mortality is too slow in most African countries to achieve the Millennium Development Goal of reducing under-five mortality by two-thirds between 1990 and 2015. Effective strategies to monitor child mortality are needed to help Governments assess and report on progress and to target their child survival programs. We present results from a test of a mortality monitoring approach based on recording of births and deaths by specially-trained community health workers (CHW) in Malawi. Mortality data collected by randomly-selected CHWs in two districts are compared to those of a “gold-standard” mortality survey. Results indicate that CHW reports under-estimated child mortality by 24% to 49%, and the level of under-estimation increased over time. The approach appears to hold some promise, however, because the vital events that were reported by the CHWs were accurate and reliable. We are now investigating further to determine patterns of errors so that CHW performance can be improved and sustained.

Agriculture is the single largest source of employment in India and nearly 60 percent of Indian population depends on agriculture for their livelihood. Today, Indian agriculture is facing crisis and the Indian farmers are trapped in a vicious circle of crop loss, low price and indebtedness that forces many farmers to commit suicide. Increasing incidence of farmer’s suicide is an issue of great concern in India. Using data from Accidental Deaths and Suicides in India, by National Crime Records Bureau (NCRB), this paper examines the incidence and trends in farmers’ suicide in India as well as states and the linkages between indebtedness and farmers’ suicides. The findings suggest that, during 2001-2011, about 184,169 farmers have lost their lives by committing suicide in India. In 2011 alone, about 14,027 farmers have committed suicide. i.e., “in every 31 minutes a farmer is committing suicide”. The western region of the country that comprises of states like Maharashtra, Madhya Pradesh, Gujarat and Rajasthan accounted for the highest number of farmer suicides (38 percent) followed by southern region (37 percent) constituted by the five contiguous states of south India viz. Kerala, Tamil Nadu, Karnataka, Andhra Pradesh and Goa.

Standard techniques of indirect estimation of child mortality use data from summary birth histories consisting of only two questions - number of children ever born alive and the number of children dead. However the estimation is based on several assumptions about fertility and mortality patterns, and rates computed for recent periods are biased. We propose and apply an innovative approach based on imputation of full birth histories onto summary birth histories. The resulting imputed full birth history is used to calculate child mortality rates using standard life table procedures. We apply the approach to data from the Malawi 2008 Population Census and the 2004 and 2010 Demographic and Health Survey datasets. Preliminary results are promising, with most of the imputed child mortality rates falling within the 95% confidence intervals of the rates directly computed from the 2010 DHS survey. In addition, choice of the existing full birth history data for the imputation did not appear to affect the resulting mortality rates computed from the imputed full birth history data.

Adult mortality in sub-Saharan Africa is very high and has been rising in the last two decades. Vital registration systems as main sources of adult mortality data are poorly developed in Kenya and many African countries. This paper explores adult mortality using data from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) in two slums in Nairobi. These data are used to estimate levels, trends, causes of and factors associated with adult mortality. Using survival analysis and standard life table methodologies, mortality is estimated over a period of 5 years. A total of 1,513 adult deaths occurred.

Life expectancy and adult mortality estimates indicate a high risk of adult death in the slums with a mortality disadvantage for women. Life expectancy at 15 at the Nairobi DSS is lower than that many of the other surveillance sites. There were mortality differentials by slum of residence, gender, wealth status, ethnicity and education. Like it has been reported in some other populations affected by HIV/AIDS, adult mortality is higher among women contrary to expectation. HIV/AIDS and injuries are leading cause of death in women and men respectively. The decline in mortally over the years is real particularly among women and is attributable to reduction in HIV/AIDS-related deaths.

Recent evidences suggest increase in adult mortality due to increase in non-communicable diseases, change in life style and age-structural transition. Though adult mortality and hospitalization in India are showing increasing trend over years, little is known about its magnitude, differentials and the risk factors and deaths by cause. This paper aims to estimate the magnitude of adult deaths by age group, sex and cause of death in India. The data for this study has been taken from multiple sources; Sample Registration System (SRS), Census of India and Special Survey on Cause of Deaths (Million Death Study). Results indicate monotonic increase in adult and old age mortality over time and shift in concentration of deaths from early ages to adult ages. Similar pattern appear for the three selected states Uttar Pradesh, Maharashtra and Kerala which are in different phases of demographic transition, though the level is different. Result shows a shift in cause of death from communicable to non-communicable diseases which is consistent with the epidemiological transition occurred in India and need urgent policy attention.

This paper identifies the spatial and temporal determinants of prostate cancer, breast cancer, and colorectal cancer, three prevalent forms of cancer in Canada. Specifically, we highlight the importance of age at residence in a cancer 'hotspot', arguing that an individual's migratory history and past places of residence are in certain circumstances better predictors of cancer in the present than current location. GIven that roughly a third of all Canadians move every five years, the ability to identify residential pathways has particular relevance. These data are the first in Canada to be able to measure the connection between current and previous places of residence and risk of contracting the three cancers mentioned above.