Uptake of family planning services in Pakistan has remained slow over the past decade despite a rapid increase in availability and awareness, indicating that social barriers may be preventing uptake. Social barriers such as opposition by family members have largely been studied qualitatively; there is a lack of quantitative evidence about the effect of different family members’ opposition on women’s intention to use contraceptives. The objective of this study was to quantitatively evaluate the effect of family members’ opposition to family planning on intention to use contraception amongst poor women in Pakistan who have physical access to family planning services.
An unmatched case control study (nested within a larger cohort study) was conducted in two public hospitals in Karachi, Pakistan. Univariable and multivariable logistic regression analyses were conducted to compare risk factors between women that were not intending to use any contraceptive methods in the future (cases) and women that were planning to use contraceptive methods (controls).
248 cases and 496 controls were included in the study. Negative contraceptive intent was associated with no knowledge of contraception (AOR = 3.79 [2.43-5.90]; p < 0.001), husband’s opposition (AOR = 21.87 [13.21-36.21]; p < 0.001) and mother-in-law’s opposition (AOR = 4.06 [1.77-9.30]; p < 0.001).
This study is the first to quantitatively assess the effect of opposition by different family members on women’s contraceptive intent in Pakistan. Our results indicate that of all family members, husband’s opposition has the strongest effect on women’s intention to use contraception, even when the women have knowledge of and physical access to family planning services.
Case–control; Family planning; Family opposition; Pakistan
There is increasing evidence that childhood vaccines have effects that extend beyond their target disease. The objective of this study was to assess the effects of routine childhood vaccines on bacterial carriage in the nasopharynx.
A cohort of children from rural Gambia was recruited at birth and followed up for one year. Nasopharyngeal swabs were taken immediately after birth, every two weeks for the first six months and then every other month. The presence of bacteria in the nasopharynx (Haemophilus influenzae, Streptococcus pneumoniae, Staphylococcus aureus) was compared before and after the administration of DTP-Hib-HepB and measles-yellow fever vaccines.
A total of 1,779 nasopharyngeal swabs were collected from 136 children for whom vaccination data were available. The prevalence of bacterial carriage was high: 82.2% S. pneumoniae, 30.6%, S.aureus, 27.8% H. influenzae. Carriage of H. influenzae (OR = 0.36; 95% CI: 0.13, 0.99) and S. pneumoniae (OR = 0.25; 95% CI: 0.07, 0.90) were significantly reduced after measles-yellow fever vaccination; while DTP-Hib-HepB had no effect on bacterial carriage.
Nasopharyngeal bacterial carriage is unaffected by DTP-Hib-HepB vaccination and reduced after measles-yellow fever vaccination.
Non-specific vaccine effects; Measles; DTP; Nasopharyngeal bacterial carriage
Aging societies worldwide propose a significant challenge to the model and organisation of the delivery of healthcare services. In developing countries, communicable and non-communicable diseases are affecting how the ageing population access healthcare; this could be due to varying reasons such as geographical barriers, limited financial support and poor literacy. New information and communication technology, such as eHealth have the potential to improve access to healthcare, information exchange and improving public and personalised medicine for elderly groups. In this article we will first frame the context of information and communication technologies in light of an aging landscape. We will also discuss the problems related to implementing the needed infrastructure for uptake of new technology, with particular emphasis on developing countries. In so doing, we highlight areas where newer technologies can serve as promising tools or vehicles to address health and healthcare-related gaps and needs of elderly people living in resource-constrained settings.
eHealth; Ageing population; Technology; Developing world; mHealth
Tuberculosis is a major disease worldwide and most research focus on risk factors for adults, although there is a marked adolescent peak in incidence. The objective of this study was to identify risk factors for tuberculosis in children aged 7 to 19.
A case control study matched by age with 169 cases and 477 controls. The study population consisted of adolescents and older children from Recife, Brazil. Cases were individuals diagnosed with tuberculosis in the control programme and controls were selected in the neighborhood of cases. Conditional logistic regression was used to identify risk factors.
Cigarette smoking increased by 50% the risk of tuberculosis but that this was not statistically significant (OR = 1.6). Other risk factors were sleeping in the same house as a case of tuberculosis (OR = 31.6), living in a house with no piped water (OR = 7.7) (probably as a proxy for bad living conditions), illiteracy (OR = 3.7) and male sex (OR = 1.8). The increase in risk with living in houses with no piped water was much more marked in males. The proportion of cases of tuberculosis attributed to contact with someone with TB was 38% and to illiteracy, lack of piped water and smoking, 20%.
Household contact with tuberculosis, social factors and male sex play the biggest role in determining risk of TB disease among children and adolescents in the study. We recommend further research on the relationship of cigarette smoking on tuberculosis in adolescents, and on whether the sex differentials are more marked in bad living conditions. Separate studies should be conducted in older children and in adolescents.
Tuberculosis; Adolescents; Risk factors; Household contacts; Socio-economic factors; Smoking
In the literature, different shapes of associations have been found between body mass index (BMI) and mortality and some of the findings were opposite to each other. The association of BMI and mortality in a single cohort has been found to be dynamic that can lead to different findings under different settings. The identified dynamic features were consistent with the heterogeneity in the literature. It is meaningful to find out whether such dynamic associations exist in other populations.
Data of six different cohorts were used for analysis and comparison. The proportional hazards assumptions for BMI in Cox models were tested to identify dynamic associations in each cohort. Time-dependent covariates Cox model was used to model the association of BMI and mortality risk as functions of follow-up time. The Cox model was applied to the pooled data with survival times censored at 5 to 40 years to show the potential impact of the dynamic association on traditional Meta-analysis.
Results and discussion
Dynamic associations were identified in six models (4 for men and 2 for women), four of which showed the same changing pattern: the elevated mortality risk for low BMI decreased while that for high BMI increased with follow-up time. When the Cox model was applied to the pooled data excluding the largest and also the shortest cohort, low BMI was but high BMI was not associated with high mortality for men with censoring at 5 years but the association for low BMI became weaker and that for high BMI became much stronger when censoring time was at 40 years. The dynamic association indicated that shorter studies tend to obtain inverse associations between BMI and mortality while longer studies tend to obtain J-shaped associations.
Different or even opposite results about body weight and mortality in the literature may be in part due to the underlying dynamic association of BMI and mortality. The dynamic features need to be taken into consideration in future studies.
Cox model; Non-proportional hazards; Time-dependent covariates Cox model; Length of follow-up
In May 2014, Middle East respiratory syndrome coronavirus (MERS-CoV) infection, with closely related viral genomes, was diagnosed in two Dutch residents, returning from a pilgrimage to Medina and Mecca, Kingdom of Saudi Arabia (KSA). These patients travelled with a group of 29 other Dutch travellers. We conducted an epidemiological assessment of the travel group to identify likely source(s) of infection and presence of potential risk factors.
All travellers, including the two cases, completed a questionnaire focussing on potential human, animal and food exposures to MERS-CoV. The questionnaire was modified from the WHO MERS-CoV questionnaire, taking into account the specific route and activities of the travel group.
Twelve non-cases drank unpasteurized camel milk and had contact with camels. Most travellers, including one of the two patients (Case 1), visited local markets, where six of them consumed fruits. Two travellers, including Case 1, were exposed to coughing patients when visiting a hospital in Medina. Four travellers, including Case 1, visited two hospitals in Mecca. All travellers had been in contact with Case 1 while he was sick, with initially non-respiratory complaints. The cases were found to be older than the other travellers and both had co-morbidities.
This epidemiological study revealed the complexity of MERS-CoV outbreak investigations with multiple potential exposures to MERS-CoV reported such as healthcare visits, camel exposure, and exposure to untreated food products. Exposure to MERS-CoV during a hospital visit is considered a likely source of infection for Case 1 but not for Case 2. For Case 2, the most likely source could not be determined. Exposure to MERS-CoV via direct contact with animals or dairy products seems unlikely for the two Dutch cases. Furthermore, exposure to a common but still unidentified source cannot be ruled out. More comprehensive research into sources of infection in the Arabian Peninsula is needed to strengthen and specify the prevention of MERS-CoV infections.
Middle East respiratory syndrome coronavirus; MERS; MERS-CoV; Exposure; Risk factors; Epidemiology
Periodic or cyclic data of known periodicity are frequently encountered in epidemiological and biomedical research: for instance, seasonality provides a useful experiment of nature while diurnal rhythms play an important role in endocrine secretion. There is, however, little consensus on how to analysis these data and less still on how to measure association or effect size for the often complex patterns seen.
A simple statistic, readily derived from Fourier regression models, provides a readily-understood measure cyclic variation in a wide variety of situations.
The coefficient of cyclic variation or similar statistics derived from the variance of a Fourier series could provide a universal means of summarising the magnitude of periodic variation.
Periodic regression; Circular data; Truncated fourier series; Coefficient of circular variation; Cosinor analysis
Oseltamivir has been registered for use as post-exposition prophylaxis (PEP) following exposure to influenza, based on studies among healthy adults. Effectiveness among frail elderly nursing home populations still needs to be properly assessed.
We conducted a randomised double-blind placebo-controlled trial of PEP with either oseltamivir (75 mg once daily) or placebo among nursing home units where influenza virus was detected; analysis was unblinded. The primary outcome was laboratory-confirmed influenza among residents in units on PEP; the secondary outcome was clinical diagnosis of influenza-like illness (ILI).
42 nursing homes were recruited, in which 17 outbreaks occurred from 2009 through 2013, two caused by influenza virus B, the others caused by influenza virus A(H3N2). Randomisation was successful in 15 outbreaks, with a few chance differences in baseline indicators. Six outbreaks were assigned to oseltamivir and nine to placebo. Influenza virus positive secondary ILI cases were detected in 2/6 and 2/9 units respectively (ns); secondary ILI cases occurred in 2/6 units on oseltamivir, and 5/9 units on placebo (ns). Logistical challenges in ensuring timely administration were considerable.
We did not find statistical evidence that PEP with oseltamivir given to nursing home residents in routine operational settings exposed to influenza reduced the risk of new influenza infections within a unit nor that of developing ILI. Power however was limited due to far fewer outbreaks in nursing homes than expected since the 2009 pandemic. (RCT nr NL92738)
Influenza outbreaks; Post-exposition prophylaxis; Nursing homes; RCT; Oseltamivir
Men having sex with men (MSM) remain the largest high-risk group involved in on-going transmission of sexually transmitted infections (STI), including HIV, in the Netherlands. As risk behaviour may change with age, it is important to explore potential heterogeneity in risks by age. To improve our understanding of this epidemic, we analysed the prevalence of and risk factors for selected STI in MSM attending STI clinics in the Netherlands by age group.
Analysis of data from the national STI surveillance system for the period 2006–2012. Selected STI were chlamydia, gonorrhoea, infectious syphilis and/or a new HIV infection. Logistic regression was used to identify factors associated with these selected STI and with overall STI positivity. Analyses were done separately for MSM aged younger than 25 years and MSM aged 25 years and older.
In young MSM a significant increase in positivity rate was seen over time (p < 0.01), mainly driven by increasing gonorrhoea diagnoses, while in MSM aged 25 and older a significant decrease was observed (p < 0.01). In multivariate analyses for young MSM, those who were involved in commercial sex were at higher risk (OR: 1.5, 95% CI: 1.2-1.9). For MSM aged 25 years and older this was not the case. Having a previous negative HIV test was protective among older MSM compared to those not tested for HIV before (OR: 0.8, 95% CI: 0.8-0.8), but not among younger MSM.
MSM visiting STI clinics remain a high-risk group for STI infections and transmission, but are not a homogenous group. While in MSM aged older than 25 years, STI positivity rate is decreasing, positivity rate in young MSM increased over time. Therefore specific attention needs to be paid towards targeted counselling and reaching particular MSM sub-groups, taken into account different behavioural profiles.
Commercial sex; Adolescents; Gay men; Epidemiology; Surveillance; Ethnicity
In many areas of clinical investigation there is great interest in identifying and validating surrogate endpoints, biomarkers that can be measured a relatively short time after a treatment has been administered and that can reliably predict the effect of treatment on the clinical outcome of interest. However, despite dramatic advances in the ability to measure biomarkers, the recent history of clinical research is littered with failed surrogates. In this paper, we present a statistical perspective on why identifying surrogate endpoints is so difficult. We view the problem from the framework of causal inference, with a particular focus on the technique of principal stratification (PS), an approach which is appealing because the resulting estimands are not biased by unmeasured confounding. In many settings, PS estimands are not statistically identifiable and their degree of non-identifiability can be thought of as representing the statistical difficulty of assessing the surrogate value of a biomarker. In this work, we examine the identifiability issue and present key simplifying assumptions and enhanced study designs that enable the partial or full identification of PS estimands. We also present example situations where these assumptions and designs may or may not be feasible, providing insight into the problem characteristics which make the statistical evaluation of surrogate endpoints so challenging.
Surrogate endpoint; Principal stratification; Causal inference; Statistical identifiability
Measures of household socio-economic position (SEP) are widely used in health research. There exist a number of approaches to their measurement, with Principal Components Analysis (PCA) applied to a basket of household assets being one of the most common. PCA, however, carries a number of assumptions about the distribution of the data which may be untenable, and alternative, non-parametric, approaches may be preferred. Mokken scale analysis is a non-parametric, item response theory approach to scale development which appears never to have been applied to household asset data. A Mokken scale can be used to rank order items (measures of wealth) as well as households. Using data on household asset ownership from a national sample of 4,154 consenting households in the World Health Survey from Vietnam, 2003, we construct two measures of household SEP. Seventeen items asking about assets, and utility and infrastructure use were used. Mokken Scaling and PCA were applied to the data. A single item measure of total household expenditure is used as a point of contrast.
An 11 item scale, out of the 17 items, was identified that conformed to the assumptions of a Mokken Scale. All the items in the scale were identified as strong items (Hi > .5). Two PCA measures of SEP were developed as a point of contrast. One PCA measure was developed using all 17 available asset items, the other used the reduced set of 11 items identified in the Mokken scale analaysis. The Mokken Scale measure of SEP and the 17 item PCA measure had a very high correlation (r = .98), and they both correlated moderately with total household expenditure: r = .59 and r = .57 respectively. In contrast the 11 item PCA measure correlated moderately with the Mokken scale (r = .68), and weakly with the total household expenditure (r = .18).
The Mokken scale measure of household SEP performed at least as well as PCA, and outperformed the PCA measure developed with the 11 items used in the Mokken scale. Unlike PCA, Mokken scaling carries no assumptions about the underlying shape of the distribution of the data, and can be used simultaneous to order household SEP and items. The approach, however, has not been tested with data from other countries and remains an interesting, but under researched approach.
Mokken scale analysis (MSA); Principal component analysis (PCA)
Epidemiology and ecology share many fundamental research questions. Here we describe how principal coordinates of neighbor matrices (PCNM), a method from spatial ecology, can be applied to spatial epidemiology. PCNM is based on geographical distances among sites and can be applied to any set of sites providing a good coverage of a study area. In the present study, PCNM eigenvectors corresponding to positive autocorrelation were used as explanatory variables in linear regressions to model incidences of eight most common cancer types in Finnish municipalities (n = 320). The dataset was provided by the Finnish Cancer Registry and it included altogether 615,839 cases between 1953 and 2010.
PCNM resulted in 165 vectors with a positive eigenvalue. The first PCNM vector corresponded to the wavelength of hundreds of kilometers as it contrasted two main subareas so that municipalities located in southwestern Finland had the highest positive site scores and those located in midwestern Finland had the highest negative scores in that vector. Correspondingly, the 165th PCNM vector indicated variation mainly between the two small municipalities located in South Finland. The vectors explained 13 - 58% of the spatial variation in cancer incidences. The number of outliers having standardized residual > |3| was very low, one to six per model, and even lower, zero to two per model, according to Chauvenet’s criterion. The spatial variation of prostate cancer was best captured (adjusted r2 = 0.579).
PCNM can act as a complementary method to causal modeling to achieve a better understanding of the spatial structure of both the response and explanatory variables, and to assess the spatial importance of unmeasured explanatory factors. PCNM vectors can be used as proxies for demographics and causative agents to deal with autocorrelation, multicollinearity, and confounding variables. PCNM may help to extend spatial epidemiology to areas with limited availability of registers, improve cost-effectiveness, and aid in identifying unknown causative agents, and predict future trends in disease distributions and incidences. A large advantage of using PCNM is that it can create statistically valid reflectors of real predictors for disease incidence models with only little resources and background information.
Cancer incidence; Finland; Principal coordinates of neighbor matrices; Spatial epidemiology
Population-based, prospective longitudinal cohort studies are considering the issues surrounding returning findings to individuals as a result of genomic and other medical research studies. While guidance is being developed for clinical settings, the process is less clear for those conducting longitudinal research. This paper discusses work conducted on behalf of The UK Cohort and Longitudinal Study Enhancement Resource programme (CLOSER) to examine consent requirements, process considerations and specific examples of potential findings in the context of the 1958 British Birth cohort. Beyond deciding which findings to return, there are questions of whether re-consent is needed and the possible impact on the study, how the feedback process will be managed, and what resources are needed to support that process. Recommendations are made for actions a cohort study should consider taking when making vital decisions regarding returning findings. Any decisions need to be context-specific, arrived at transparently, communicated clearly, and in the best interests of both the participants and the study.
Individual genetic research findings; Longitudinal population cohort; Ethics; Policy
The aim of this study was to provide a novel approach for estimating the incidence of renal cancer in Germany by using hospitalization data from the years 2005–2006 and to compare these estimates with incidence rates from cancer registries.
We used nationwide hospitalization data from the years 2005–2006 including 34.2 million hospitalizations. We used three definitions of potential incident renal cancer cases: 1) a main or secondary diagnosis of renal cancer and a partial or total nephrectomy; 2) a main diagnosis of renal cancer and a partial or total nephrectomy; and 3) a main diagnosis of renal cancer (without a secondary diagnosis of renal pelvis cancer) and a partial or total nephrectomy. In addition, we used cancer registry data for comparison of rates.
Hospitalization data to which definition 2 applied provided incidence rate estimates nearly identical to those provided by the cancer registries (when the cases registered from death certificates only were excluded). Age-standardized (European standard population) incidence rates based on hospitalization data and cancer registry data were 15.6 per 100 000 and 15.7 per 100 000 among men and 8.0 per 100 000 and 7.6 per 100 000 among women respectively. Cancer registry-based incidence rates were lower especially among those federal states with an estimated completeness of registration below 90% (Berlin and Saxony-Anhalt).
Representative hospitalization data can be used to estimate incidence rates of renal cancer. We propose that incidence rates can be estimated by hospitalization data if 1) the primary treatment is performed during an in-hospital stay and 2) nearly all patients undergo a defined surgical procedure that is not repeated for the treatment of the same cancer. Our results may be useful for countries with no or incomplete cancer registration or for countries that use hospitalization data to provide a representative incidence of renal cancer.
Kidney neoplasms; Incidence; Registries; Hospitalization; Nephrectomy
The 21st century has seen the rise of Internet-based participatory surveillance systems for infectious diseases. These systems capture voluntarily submitted symptom data from the general public and can aggregate and communicate that data in near real-time. We reviewed participatory surveillance systems currently running in 13 different countries. These systems have a growing evidence base showing a high degree of accuracy and increased sensitivity and timeliness relative to traditional healthcare-based systems. They have also proven useful for assessing risk factors, vaccine effectiveness, and patterns of healthcare utilization while being less expensive, more flexible, and more scalable than traditional systems. Nonetheless, they present important challenges including biases associated with the population that chooses to participate, difficulty in adjusting for confounders, and limited specificity because of reliance only on syndromic definitions of disease limits. Overall, participatory disease surveillance data provides unique disease information that is not available through traditional surveillance sources.
Dengue; Influenza-like illness; Participatory surveillance; Participatory surveillance system; Disease surveillance; Public health
The asset index is often used as a measure of socioeconomic status in empirical research as an explanatory variable or to control confounding. Principal component analysis (PCA) is frequently used to create the asset index. We conducted a simulation study to explore how accurately the principal component based asset index reflects the study subjects’ actual poverty level, when the actual poverty level is generated by a simple factor analytic model. In the simulation study using the PC-based asset index, only 1% to 4% of subjects preserved their real position in a quintile scale of assets; between 44% to 82% of subjects were misclassified into the wrong asset quintile. If the PC-based asset index explained less than 30% of the total variance in the component variables, then we consistently observed more than 50% misclassification across quintiles of the index. The frequency of misclassification suggests that the PC-based asset index may not provide a valid measure of poverty level and should be used cautiously as a measure of socioeconomic status.
Principal component analysis; Socio-economic status; Asset index; Wealth index
The study of non-atopic asthma/wheeze in children separately from atopic asthma is relatively recent. Studies have focused on single risk factors and had inconsistent findings.
To review evidence on factors associated with non-atopic asthma/wheeze in children and adolescents.
A review of studies of risk factors for non-atopic asthma/wheeze which had a non-asthmatic comparison group, and assessed atopy by skin-prick test or allergen-specific IgE.
Studies of non-atopic asthma/wheeze used a wide diversity of definitions of asthma/wheeze, comparison groups and methods to assess atopy. Among 30 risk factors evaluated in the 43 studies only 3 (family history of asthma/rhinitis/eczema, dampness/mold in the household, and lower respiratory tract infections in childhood) showed consistent associations with non-atopic asthma/wheeze. No or limited period of breastfeeding was less consistently associated with non-atopic asthma/wheeze. The few studies examining the effects of overweight/obesity and psychological/social factors showed consistent associations. We used a novel graphical presentation of different risk factors for non-atopic asthma/wheeze, allowing a more complete perception of the complex pattern of effects.
More research using standardized methodology is needed on the causes of non-atopic asthma.
Non-atopic asthma; Non-atopic wheeze; Risk factors; Mould; Respiratory infections
It is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa.
Our results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age.
We show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures.
Pandemic influenza H1N1; Reproductive number; Infectious disease
Maternal morbidity is more common than maternal death, and population-based estimates of the burden of maternal morbidity could provide important indicators for monitoring trends, priority setting and evaluating the health impact of interventions. Methods based on lay reporting of obstetric events have been shown to lack specificity and there is a need for new approaches to measure the population burden of maternal morbidity. A computer-based probabilistic tool was developed to estimate the likelihood of maternal morbidity and its causes based on self-reported symptoms and pregnancy/delivery experiences. Development involved the use of training datasets of signs, symptoms and causes of morbidity from 1734 facility-based deliveries in Benin and Burkina Faso, as well as expert review. Preliminary evaluation of the method compared the burden of maternal morbidity and specific causes from the probabilistic tool with clinical classifications of 489 recently-delivered women from Benin, Bangladesh and India.
Using training datasets, it was possible to create a probabilistic tool that handled uncertainty of women’s self reports of pregnancy and delivery experiences in a unique way to estimate population-level burdens of maternal morbidity and specific causes that compared well with clinical classifications of the same data. When applied to test datasets, the method overestimated the burden of morbidity compared with clinical review, although possible conceptual and methodological reasons for this were identified.
The probabilistic method shows promise and may offer opportunities for standardised measurement of maternal morbidity that allows for the uncertainty of women’s self-reported symptoms in retrospective interviews. However, important discrepancies with clinical classifications were observed and the method requires further development, refinement and evaluation in a range of settings.
Maternal health; Morbidity; Developing countries; Pregnancy; Childbirth; Bayesian analysis; Africa; Asia
Lot Quality Assurance Sampling (LQAS) surveys have become increasingly popular in global health care applications. Incorporating Bayesian ideas into LQAS survey design, such as using reasonable prior beliefs about the distribution of an indicator, can improve the selection of design parameters and decision rules. In this paper, a joint frequentist and Bayesian framework is proposed for evaluating LQAS classification accuracy and informing survey design parameters. Simple software tools are provided for calculating the positive and negative predictive value of a design with respect to an underlying coverage distribution and the selected design parameters. These tools are illustrated using a data example from two consecutive LQAS surveys measuring Oral Rehydration Solution (ORS) preparation. Using the survey tools, the dependence of classification accuracy on benchmark selection and the width of the ‘grey region’ are clarified in the context of ORS preparation across seven supervision areas. Following the completion of an LQAS survey, estimation of the distribution of coverage across areas facilitates quantifying classification accuracy and can help guide intervention decisions.
Acceptance sampling; LQAS; Survey design
Great progress has been made in mathematical models of cholera transmission dynamics in recent years. However, little impact, if any, has been made by models upon public health decision-making and day-to-day routine of epidemiologists. This paper provides a brief introduction to the basics of ordinary differential equation models of cholera transmission dynamics. We discuss a basic model adapted from Codeço (2001), and how it can be modified to incorporate different hypotheses, including the importance of asymptomatic or inapparent infections, and hyperinfectious V. cholerae and human-to-human transmission. We highlight three important challenges of cholera models: (1) model misspecification and parameter uncertainty, (2) modeling the impact of water, sanitation and hygiene interventions and (3) model structure. We use published models, especially those related to the 2010 Haitian outbreak as examples. We emphasize that the choice of models should be dictated by the research questions in mind. More collaboration is needed between policy-makers, epidemiologists and modelers in public health.
Relative risk is a summary metric that is commonly used in epidemiological investigations. Increasingly, epidemiologists are using log-binomial models to study the impact of a set of predictor variables on a single binary outcome, as they naturally offer relative risks. However, standard statistical software may report failed convergence when attempting to fit log-binomial models in certain settings. The methods that have been proposed in the literature for dealing with failed convergence use approximate solutions to avoid the issue. This research looks directly at the log-likelihood function for the simplest log-binomial model where failed convergence has been observed, a model with a single linear predictor with three levels. The possible causes of failed convergence are explored and potential solutions are presented for some cases.
Among the principal causes is a failure of the fitting algorithm to converge despite the log-likelihood function having a single finite maximum. Despite these limitations, log-binomial models are a viable option for epidemiologists wishing to describe the relationship between a set of predictors and a binary outcome where relative risk is the desired summary measure.
Epidemiologists are encouraged to continue to use log-binomial models and advocate for improvements to the fitting algorithms to promote the widespread use of log-binomial models.
Log-binomial; Non-convergence; Failed convergence; Relative risk; Method of maximum likelihood; Log relative risk; Likelihood estimation; Maximum likelihood estimates; Logistic regression alternatives
Effective interventions require evidence on how individual causal pathways jointly determine disease. Based on the concept of systems epidemiology, this paper develops Diagram-based Analysis of Causal Systems (DACS) as an approach to analyze complex systems, and applies it by examining the contributions of proximal and distal determinants of childhood acute lower respiratory infections (ALRI) in sub-Saharan Africa.
Diagram-based Analysis of Causal Systems combines the use of causal diagrams with multiple routinely available data sources, using a variety of statistical techniques. In a step-by-step process, the causal diagram evolves from conceptual based on a priori knowledge and assumptions, through operational informed by data availability which then undergoes empirical testing, to integrated which synthesizes information from multiple datasets. In our application, we apply different regression techniques to Demographic and Health Survey (DHS) datasets for Benin, Ethiopia, Kenya and Namibia and a pooled World Health Survey (WHS) dataset for sixteen African countries. Explicit strategies are employed to make decisions transparent about the inclusion/omission of arrows, the sign and strength of the relationships and homogeneity/heterogeneity across settings.
Findings about the current state of evidence on the complex web of socio-economic, environmental, behavioral and healthcare factors influencing childhood ALRI, based on DHS and WHS data, are summarized in an integrated causal diagram. Notably, solid fuel use is structured by socio-economic factors and increases the risk of childhood ALRI mortality.
Diagram-based Analysis of Causal Systems is a means of organizing the current state of knowledge about a specific area of research, and a framework for integrating statistical analyses across a whole system. This partly a priori approach is explicit about causal assumptions guiding the analysis and about researcher judgment, and wrong assumptions can be reversed following empirical testing. This approach is well-suited to dealing with complex systems, in particular where data are scarce.
Africa; Children; Acute lower respiratory infections; Pneumonia; Health determinants; Causal diagrams; Multi-factorial causality; Systems epidemiology; Social epidemiology; Environmental epidemiology
Individual-level data pooling of large population-based studies across research centres in international research projects faces many hurdles. The BioSHaRE (Biobank Standardisation and Harmonisation for Research Excellence in the European Union) project aims to address these issues by building a collaborative group of investigators and developing tools for data harmonization, database integration and federated data analyses.
Eight population-based studies in six European countries were recruited to participate in the BioSHaRE project. Through workshops, teleconferences and electronic communications, participating investigators identified a set of 96 variables targeted for harmonization to answer research questions of interest. Using each study’s questionnaires, standard operating procedures, and data dictionaries, harmonization potential was assessed. Whenever harmonization was deemed possible, processing algorithms were developed and implemented in an open-source software infrastructure to transform study-specific data into the target (i.e. harmonized) format. Harmonized datasets located on server in each research centres across Europe were interconnected through a federated database system to perform statistical analysis.
Retrospective harmonization led to the generation of common format variables for 73% of matches considered (96 targeted variables across 8 studies). Authenticated investigators can now perform complex statistical analyses of harmonized datasets stored on distributed servers without actually sharing individual-level data using the DataSHIELD method.
New Internet-based networking technologies and database management systems are providing the means to support collaborative, multi-center research in an efficient and secure manner. The results from this pilot project show that, given a strong collaborative relationship between participating studies, it is possible to seamlessly co-analyse internationally harmonized research databases while allowing each study to retain full control over individual-level data. We encourage additional collaborative research networks in epidemiology, public health, and the social sciences to make use of the open source tools presented herein.
Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda.
To determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations.
The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications.
We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.
Cluster-LQAS; Lot quality assurance sampling; Program evaluation; Survey; Community health workers