Methods for assessing cause and effect in population sciences

Breast cancer is the most common cancer in females worldwide. Breast cancer has large number of etiological factors increasing the risk. An increased risk of breast cancer in women with a family history of breast cancer has been demonstrated by many studies using a variety of study design.
The objective is to quantified the risk of breast cancer associated with a family history of the disease, and to summerize the evidence from these studies. Data sources: Fifteen case-control studies published between 2000-2010 were retrieved through Pubmed and previous reviews were included in the meta-analysis. Positive family history was taken as risk category. The results of fifteen case-control studies pooled to determine the relationship between family history and risk of breast cancer. The overall odds ratio along with 95% CI of fixed effect model was 1.63(1.44-1.84) and the pooled estimate of random effect model was 2.20(1.51-3.23). Test of heterogeneity Q = 104.81 (P<.001) statistically different and I2 was 86.6%. Conclusion: A patient family history may aid clinical diagnosis and contribute to disease risk assessment and prediction. Therefore it is possible that identifying people with a family history of disease could act as a additional motivator for them to change their lifestyle or participate in disease screening.

In many developing countries, vital statistics are non-existent, in those where they exist, the data are inadequate and defective that can not be relied upon as a true measure of vital events (births, deaths, marriages, etc.). It is expected that all under-5 children births covered in 2008 Nigeria NDHS ought to have been registered; but bared 30% of them was registered.
The study will employ secondary data. Secondary data for the study will be extracted from the 2008 Nigeria Demographic and Health survey (NDHS). Data on women aged 15-49 years that have had at least a child within the last five years will be extracted for 33,385 women sampled from the NDHS (2008). Data will be analyzed using appropriate descriptive and inferential statistics.
It is expected that there will be relationship between mothers’ demographic characteristics and under-5 birth registration.
The findings from the study are expected to give more insight to the influence of the mothers’ demographic characteristics (parity, proportion of dead children, birth interval and marital status) on under-5 birth registration.. It will also help in building a policy frame work to improve quality and quantity of vital registration, especially birth registration

This paper aims to examine the net impact of ANC visits on subsequent utilization of child immunization after removing the presence of selection bias using NFHS 2005-06 data. We have used propensity score matching method with a counterfactual model. Using matched sample analysis result shows that child immunization among the groups of women who have completed 1-2 ANC visits and 3+ visits was about 13 percent and 19 percent respectively, higher than the group of women who have not made any ANC visit. Findings of nearest neighbor matching with replacement method, indicate, selection bias present in data set leads to overestimates positive effects of ANC visits. Mantel-Haenszel bounds method suggest that if around 19 percent bias would be involved in the result then also we could observe true positive effect of 1-2 ANC visits on child immunization. It indicates that antenatal clinics are the conventional platforms for educating pregnant women on the benefits of immunization.

Improving accountability for commitments made in maternal and neonatal health has become a global priority in the race to accelerate progress on MDGs 4 and 5. At a time when tax-payers are also demanding strong, quantitative evidence that aid programmes represent value for money, new methods to attribute tangible impact to such accountability programmes are required. Conducting this type of analysis is challenging for accountability programmes. The outcomes of accountability activities are not easily quantifiable and the overall approach is still in the process of building an evidence-base.

In this article, we describe a methodology to project the number of maternal and neonatal lives saved by a programme aiming to strengthen accountability for MNH. Extensive evidence on the effectiveness of various bio-medical interventions for maternal and neonatal health has been incorporated within the Lives Saved Tool (LiST) software, developed by the Johns Hopkins School of Public Health. Our method extends the application of LiST beyond its bio-medical scope by assuming that greater accountability, through the more efficient use of health resources, causes a proportional increase in the coverage of all life-saving MNH services.

With about 1 percent of the global population, the Democratic Republic of Congo (DRC) has the third highest diarrheal morbidity among under-five children worldwide. This study aimed to describe trends in diarrhea prevalence by selected socioeconomic characteristics; identify the sources of variation, and assess contribution of changes the DRC. The study used information on 9,748 children from the 2001 Multiple Indicators Cluster Survey and 7,987 children from the 2007 Demographic and Health Survey. Statistical techniques include trends analysis, decomposition method and fixed effect regression models.
Findings revealed significant decrease in diarrhea prevalence in all socioeconomic categories considered. Changes in behavior and/or in public health policy seem to be likely the main source of the observed changes, as there were no significant changes in diarrhea prevalence associated with variation of the population structure. However, the decrease in diarrhoea prevalence in the DRC contrasts with the generalized humanitarian crisis, deterioration in environmental conditions and population poverty observed in the country during the same study period. These findings suggest further investigations through district or provincial based studies to validate findings from national surveys.

Systematic anomalous case analysis (SACA) is a form of mixed-method research in which existing data are used to identify and conduct subsequent examination of cases that do not exhibit the expected behavior, for the purpose of refining social theories and measurement strategies. Using intergenerational longitudinal cohort data from the Cebu Longitudinal Health and Nutrition Survey (CLHNS) in the Philippines, we used social contextual variables (household, peer, family, and young adults’ demographics and sexual attitudes) from adolescents who participated in the 1998 survey to predict the number of living children they reported by 2009 (ages 25-26). We then calculate Pearson’s residuals to identify statistical outliers (anomalous cases) from the multivariate, gender-disaggregated models. In the next phase of this study to be conducted in Spring 2013, we will conduct in-depth interviews with a subsample of these anomalous cases, as well as normative cases, to identify ways in which subsequent investigations and data collection instruments may better theorize and measure fertility among young adults in this and other settings.

Both demographic transition theory and evolutionary life history theory propose that fertility responds to changes in mortality. This relationship exists at population level, and may be mediated by psychological mechanisms causing individuals to lower fertility as mortality rates fall. Controlled experiments have found that individuals faced with cues of high mortality express increased/accelerated fertility preferences, and reduced support for birth control policies. UK research shows that socioeconomically deprived people are both less likely to terminate pregnancies and are less accepting of abortion than wealthier people. Health disparities may mean that perceptions of increased mortality and morbidity for poorer people might decrease motivation to terminate pregnancy when young. A randomised controlled online experiment here tests if mortality cues influence abortion attitudes. We predict that:
1) Mortality salience cues will a) decrease reported approval of abortion b) increase reported motivation to have children sooner.
2) Longevity cues will a) increase reported approval of abortion b) decrease reported motivation to have children sooner.
A sample of 256 individuals from England and Wales, aged 18-25, 50% men, 50% women will complete online questionnaires, and data will be analysed using GLM and MANOVA techniques.

This paper is addressing the modelling of old age mortality and its
dependence of factors earlier in life. We argue for alternatives
to the widely used proportional hazards (PH) model, especially Cox
regression. There are several reasons for this. First, it is well known
that old age mortality very often is well described by the Gompertz
distribution. Second, accelerated failure time (AFT) models can be
expressed as linear models, which is important when interest lies in the analysis of mediating effects in the analysis of the impact of early-life factors on old-age mortality. Third, the results of an AFT model fit is easier and more intuitive to interpret in tems of years lost or gained,
compared to the PH model fit which reports relative risks. Fourth, contrary to "common knowledge", the family of Gompertz distributions is both a collection of PH families and a collection of AFT families, which we demonstrate in the paper. For instance, Kleinbaum and Klein (2005), in their text book on survival analysis, writes: "The Gompertz model is a parametric PH model but not an AFT model". This mistake is reiterated by other authors.

Allostatic load has been conceptualised as a biological burden induced by chronic stress which predicts health outcomes. Few studies have investigated whether this is the case. We investigate reciprocal associations between allostatic load, limiting long-term illness and self-rated health using cross-lagged longitudinal modelling. The sample included men and women aged 52+ who participated in Wave 2 (2004) and Wave 4 (2006) of English Longitudinal Study of Ageing (n = 4,688 in wave 4). Allostatic load was measured with nine biomarkers using a multisystem summary approach. Self-rated health was measured using a global 5-point indicator; the measure of limiting tong-term illness was dichotomous. Autoregressive cross-lagged models between allostatic load, limiting long-term illness and self-rated health in waves 2 and 4 were tested. Models were adjusted for age, gender and education in wave 2, and for time-varying variables of being married, wealth, physical activity, smoking, and social support. Allostatic load predicted limiting long-term illness. The association between allostatic load and self-rated health was reciprocal, and the strength of the estimates suggested that the path from poor self-rated health to low allostatic load score was stronger than the path from low allostatic load to poor self-rated health.

The association between maternal smoking during pregnancy and offspring’s birthweight reduction is very well established. However, less is known about the effect of smokeless tobacco on birthweight and about the specific mechanism through which smoking reduces birthweight. This paper aimed at filling these knowledge gaps by studying the effect of maternal Swedish snuff use during pregnancy (SUDP) on offspring’s birthweight. Swedish snuff is grounded tobacco without any toxic substance added, which provides us with the opportunity to isolate the effect of nicotine on birthweight. With data from the Swedish Medical Birth Register (2002-2009), we applied a sibling analysis using multilevel linear regression model (50,497 siblings nested within 23,443 mothers). This design has potential to study causal associations since, it accounts for unknown genetic and environmental characteristics of the mother associated to both tobacco habits and offspring birthweight. Our results show that SUDP does not have an effect on birthweight reduction.