Multiple faces of the health transition

In the framework of the unfavourable mortality trends observed in the former Soviet republics, Caucasian countries experience specific evolution. In this region, civil registration of vital events remains not complete. We re-estimated mortality levels and trends in Armenia and Georgia since the early 1980s, confirming specific mortality patterns, but similar in both countries. However, gender differences are greater in Georgia than in Armenia. The aim of this paper is to explore the reasons for these differences: looking at all ages, but more specifically at adult mortality for which data are more reliable and where we suspect a major role of alcohol-related mortality that is higher in Georgia than in Armenia.

Japan’s extraordinary low level of old-age mortality is well documented compared to other countries. Less well understood is how the health of the Japanese older population fares relative to other countries, and whether differences persist in consistent ways across major domains of morbidity – chronic diseases, functioning and disability. To the extent that Japan’s low mortality is brought about by the postponement of disease and its sequelae, we would expect to see cross-national differences in the timing of morbidity across all major domains. Here, we compare Japanese mortality and morbidity to the mortality and morbidity experiences of older populations in Taiwan and the US. We draw on demographic models of healthy life expectancy to summarize how major domains of morbidity combine with all-cause mortality to produce cross-national differences in the years of life with and without major types of morbidity. We use a simulation approach to evaluate how morbidity/mortality timing shapes the cross-national differences in healthy life expectancy. By using Japan as a standard, this study will provide unique insights into the possible morbidity avenues by which a population’s health can be significantly improved.

Among the millennium development goals (MDG) is the one concerning the reduction of maternal mortality by three quarters between 1990 and 2015. Probably the most outstanding failure in the attainment of MDG is the reduction of maternal mortality. Both international agencies as well as national governments recognize that it is very unlikely for most developing countries that this goal will be achieved. There has been a sort of stalling in the reduction of maternal mortality, perhaps because the capacity and infrastructure of some countries does not increase in line with the requirements for such ambitious reductions.

This paper investigates whether the Matthew Effect occurs or not for maternal mortality decline, with the implication of widening the existing gap between developed and developing countries.

The Matthew Effect is a phenomenon in which inequalities increase. It was initially labeled by Merton (1968) making reference to a biblical phrase: “Unto every one that hath shall be given, and he shall have abundance; but from him that hath not shall be taken away even that which he hath” (Matthew 25:29).
To evaluate this hypothesis we take the experience of around 180 countries from all over the world, applying one statistical test to verify empirically.

Disability is conceptualized as arising from the interaction of a person’s functional status with their physical, cultural, and policy environments. If the environment one lives in is designed for the full range of human functioning and incorporates appropriate accommodations and support mechanisms, then people with functional limitations would be able to fully participate in society. The equalization of opportunities is among the major targets of the UN Convention on the Rights of Person’s with Disabilities – and was selected as the purpose for developing the set of questions described here. The International Classification of Functioning, Disability and Health (ICF) developed by the World Health Organization has played a pivotal role in realizing this conceptualisation of disability. This approach to disability has required the development of new measurement tools for use in both censuses and surveys. Earlier impairment-based approaches to disability measurement focused on medical conditions and asked some variation of the question: Do you have a disability? The current focus of measurement expresses the social model of disability and has shifted to eliciting experienced difficulties in functioning in a set of basic action domains.

We examine the mortality transition by placing country-specific trajectories on a landscape defined by demographic, social, and economic factors. We identify dimensions of a demographic ‘space’ that different countries have occupied through time. A population’s space is defined by life expectancy, GRR, latitude, and Gross Domestic Product per capita. We also quantify changes in the selection landscape by examining associations between mortality and Fisher’s reproductive value, Crow’s opportunity for selection, and Ryder’s demographic metabolism (the sum of the intrinsic birth and death rates). The large amounts of data (9 countries spanning 315 to 564 years) and the features of our demographic landscapes identify patterns among economic, selective, and historical factors that cannot typically be revealed in smaller scale analyses. For example, mortality reduction leads to changes in the relative variation of demographic traits, altering the potential for selection. We find that the potential for selection acting on longevity decreases dramatically as probability of survival to adulthood increases and that fertility selection is greatest when fertility is lowest. Likewise, countries often follow very different trajectories across the landscapes as they go through the mortality transition.

Since 1999 IBGE – the Brazilian Institute of Geography and Statistics – is annually publishing mortality tables for the Brazilian population, according to gender and age, the first table in the series being published in 1980. The latest 2011 mortality table is due at the end of 2012 and will crucially include data from the latest 2010 Brazilian Census. Census data in Brazil is collected once in a decade and therefore the latest data are important to update any past study that involved extrapolation in the future.

In this work we use past data and the latest census data to run an analysis of Brazilian life-expectancy using the Lee-Carter model. Such model allows to forecast a trend in life expectancy as well as to estimate probability intervals for deviations from the trend, using a stochastic factor. The data we obtain can be used for scenario analysis and policy making.

We also develop a free computer based tool to run the Lee-Carter model analysis from a given set of mortality table inputs.

Andrea Werdecker, Ulrich Mueller
Regional Differences in Diabetes Mellitus Type 2 (T2DM) Morbidity and Mortality in North America and Europe:
Reality or Measurement Artefact?
Short Abstract Prepared for Presentation at IUSSP 2013
Cancer, Metabolic Syndrome, Dementias are the dominant health problems in aging societies, T2DM advancing all three. The considerable regional differences in T2DM prevalence may be questioned, and so are estimations of undiagnosed T2DM. OECD multiplied observed prevalence for the UK by 1,5 and doubled for other European countries. In the US, lowest diabetes prevalence is found in Midwest, Northeast, highest in Southern, Appalachian states, with variation 150% as compared to the lowest, applying for men and women, younger and older (60+) alike. In European Union, prevalence variation is even higher at 180-200% highest in Portugal, Germany vs. UK, Sweden. However, when correlated with regional obesity rates or ischemic heart disease and stroke mortality, this variation seems plausible, being good news since it justifies regional prevention strategies. Estimating undiagnosed T2DM by elevated Fastening Plasma Glucose and HbA1c alone, however, leads to much lower total prevalence than still assumed in authoritative sources.

For a long time the basic assumption has linked the change in life expectancy between the Flemish and French talking regions in Belgium to the uneven economic development between the regions and to the evolution in relative wealth and socio-economic composition of the population. There is no doubt of the importance of socio-economic factors on population health and mortality, but the pure association over time does not explain the particular mechanisms at work. Moreover, when analysing the shift in age specific mortality between the regions, the age pattern tells another story, not exactly in sync with the shift in socio-economic wealth between north and south. Using historical mortality data and recent individual cause specific mortality data, we try to explore more in depth the factors that caused this inversion in life expectancy and that are still contributing to the enduring lower life expectancy in the Walloon region compared to the Flemish region. The insights based on the detailed analysis of this process illustrates the Belgian case and add insight in the complexity of the general process of the health transition.

In low income countries problems of basic health services go up. In affluent countries where basic service was generally accessible, question of access concern the degree of comprehensive that can be offered by health care system. Access to health care is a “basic human right and social goal so all individuals are consider to be entitled of economics benefits of the wider community does not necessary require that they should receive it”. Health infrastructures of Odisha are far from requirements and the outcomes of health are far from satisfactory. This is because of, both, inadequate health care facilities to the population as well as due to insufficient affordable capacity of majority of the people. There is a heavy burden of diseases in Odisha. Among all health indicators fertility and mortality rate are playing an important role in health sector. These ratios are consistently high in India compare to other countries. Not only in other country but also inside the country this is high in Odisha. Low Fertility rate but high mortality rate, why? What would be the reasons behind this? Main objective of this study is to analyse the trends between fertility and mortality in India as well as Odisha.

Malaria is a major risk to lives and development in Nigeria and other pan endemic regions. Pregnant women and children are at greater risk of this vector borne disease. Several efforts had been put in place towards prevention and treatment but negligible success hard been recorded.
Studies on malaria related maternal mortality in Nigeria have focused largely on preventive behaviours and healthcare providers’ knowledge of treatment regimen. Negligible attention has been paid to adherence of the care seekers to treatment in relevant contexts. Employing both qualitative and quantitative data collection techniques, the study established that adherence to malaria treatment among pregnant women in the area was influenced by social, residential and demographic factors in both rural and urban areas of the state. Expectant mothers without formal education reported high adherence to medication (r=-631 p< .034) than those of higher educational status, indicating that the level of education does not necessarily influence adherence to medication.