Population-level Outcomes Research Articles

The Comparative Toxicogenomics Database: update 2019.

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) is a premier public resource for literature-based, manually curated associations between chemicals, gene products, phenotypes, diseases, and environmental exposures. In this biennial update, we present our new chemical–phenotype module that codes chemical-induced effects on phenotypes, curated using controlled vocabularies for chemicals, phenotypes, taxa, and anatomical descriptors; this module provides unique opportunities to explore cellular and system-level phenotypes of the pre-disease state and allows users to construct predictive adverse outcome pathways (linking chemical–gene molecular initiating events with phenotypic key events, diseases, and population-level health outcomes). We also report a 46% increase in CTD manually curated content, which when integrated with other datasets yields more than 38 million toxicogenomic relationships. We describe new querying and display features for our enhanced chemical–exposure science module, providing greater scope of content and utility. As well, we discuss an updated MEDIC disease vocabulary with over 1700 new terms and accession identifiers. To accommodate these increases in data content and functionality, CTD has upgraded its computational infrastructure. These updates continue to improve CTD and help inform new testable hypotheses about the etiology and mechanisms underlying environmentally influenced diseases.

Application of dynamic modeling for survival estimation in advanced renal cell carcinoma.

ObjectiveIn oncology, extrapolation of clinical outcomes beyond trial duration is traditionally achieved by parametric survival analysis using population-level outcomes. This approach may not fully capture the benefit/risk profile of immunotherapies due to their unique mechanisms of action. We evaluated an alternative approach—dynamic modeling—to predict outcomes in patients with advanced renal cell carcinoma. We compared standard parametric fitting and dynamic modeling for survival estimation of nivolumab and everolimus using data from the phase III CheckMate 025 study.MethodsWe developed two statistical approaches to predict longer-term outcomes (progression, treatment discontinuation, and survival) for nivolumab and everolimus, then compared these predictions against follow-up clinical trial data to assess their proximity to observed outcomes. For the parametric survival analyses, we selected a probability distribution based on its fit to observed population-level outcomes at 14-month minimum follow-up and used it to predict longer-term outcomes. For dynamic modeling, we used a multivariate Cox regression based on patient-level data, which included risk scores, and probability and duration of response as predictors of longer-term outcomes. Both sets of predictions were compared against trial data with 26- and 38-month minimum follow-up.ResultsBoth statistical approaches led to comparable fits to observed trial data for median progression, discontinuation, and survival. However, beyond the trial duration, mean survival predictions differed substantially between methods for nivolumab (30.8 and 51.5 months), but not everolimus (27.2 and 29.8 months). Longer-term follow-up data from CheckMate 025 and phase I/II studies resembled dynamic model predictions for nivolumab.ConclusionsDynamic modeling can be a good alternative to parametric survival fitting for immunotherapies because it may help better capture the longer-term benefit/risk profile and support health-economic evaluations of immunotherapies.

Evaluating the implementation of the Inverse Care Law Programme in Wales, a population based intervention in primary care

IntroductionThe Inverse Care Law (ICL) programme in Wales was setup to identify people in deprived communities at risk of cardiovascular disease (CVD) through a health check; offering lifestyle and medical intervention as appropriate. Evaluation of this programme to tackle health inequalities ensuring services are available is vital.
 Objectives and ApproachTo evaluate the uptake and long term outcomes of the programme, using longitudinal evidence-based results, it was necessary to develop an efficient and cost effective approach with a readily available source of data. To achieve this, the Welsh Longitudinal General Practice (WLGP) data held in Secure Anonymised Information Linkage (SAIL) databank was utilised, with programme-specific code deployed within primary care at the point of the health check, which identified the intervention, potential CVD risks, referrals and any follow-up. Lifestyle risk factors could be evaluated such as poor diet, physical inactivity, smoking and high alcohol intake.
 ResultsUtilising routine data sources and reproducible SQL (Standard Query Language), we evaluated the programme initialisation between February 2015 and November 2016, and found of 55 General Practices who participated, 31 of 35 in Aneurin Bevan (AB) and 17 of 20 in Cwm Taf (CT) University Health Boards, providing data which allowed identification of the health check and associated outcomes of interest in the routine data, with 3 (2 AB, 1 CT) since delivering. There are ongoing evaluations on the various risk factors longitudinally as well as the overall implementation of the programme itself, with this collaborative approach succeeding in utilising existing powerful data linkage within the SAIL databank to identify our intervention and facilitate long-term follow-up at an individual level using robust information governance mechanisms.
 Conclusion/ImplicationsLessons learned and challenges encountered are being fed back as part of our evaluation, with further work assessing the long term population level outcomes and impact of the health check and services provided across these deprivation groups, informing and refining programme delivery of similar work across Wales in the future.

Clinical Outcomes After Pulmonary Metastasectomy for Melanoma: A Population-Based Study

Resection of pulmonary metastases is an important treatment option for patients with oligometastatic melanoma. There are currently no published data available on population-level outcomes. Here, we report outcomes of a registry-based study of patients who underwent lung resection for cutaneous melanoma metastases. The study population was all cases of cutaneous melanoma in Ontario, Canada, with resection of pulmonary metastases from 2004 to 2012. Melanoma cases were identified using the population-based Ontario Cancer Registry and were linked with hospital records to identify thoracic surgery. Pathology reports from the Ontario Cancer Registry were used to confirm the histology of resected lesions and determine tumor-related prognostic factors. Overall survival was described, and multivariable Cox regression utilized. Ninety-nine patients underwent resection of cutaneous melanoma lung metastases. Mean age was 58 years. Sixty-day postoperative mortality was 0%. Two-year and 5-year overall survival from time of resection was 44%and 21%, respectively (95% confidence intervals: 34% to 54% and 12% to 31%, respectively). Two-year overall survival was 57% for lesion size less than 1.5 cm; 43% for lesion size 1.5 to 2.2 cm; and 35% for lesion size 2.2 cm or greater (confidence intervals: 35% to 73%, 26% to 59%, and 19% to 52%, respectively). In the multivariable stepwise selected model, the only significant variable was size 2.3 cm or greater (hazard ratio 1.64, confidence interval: 1.001 to 2.68). Greater lesion size was correlated with positive margin status (p= 0.04); there were no survivors beyond 2years with positive margins. In this unselected population-based study, 21% of patients were 5-year survivors after pulmonary metastasectomy for melanoma. There was worse survival with greater lesion size. Greater lesion size was associated with positive margin status.

Persistent Disparities in Survival for Patients with Glioblastoma

Glioblastoma (GBM) is the most common malignant primary brain cancer in adults. Recent efforts have elucidated genetic features of tumor cells and thus enhanced our knowledge of GBM pathophysiology. The most recent clinical trials report median overall survival between 14 and 20 months. However, population level outcomes are quite variable and there is a paucity of such data within the literature. Three hundred seventy-two patients with GBM were diagnosed in the Canadian province of British Columbia between January 2013 and January 2015 and 278 patients had surgery. Of these, 268 had surgery in British Columbia and we have performed a retrospective review of their survival outcomes. Our results indicate a median age of 61.8 years at time of diagnosis, with a slight preponderance of male patients. The median overall survival was 10 months for patients in our cohort. As expected, patients older than the age of 65 and those with worse initial Karnofsky Performance Status scores had a poorer prognosis. Moreover, we have found extent of resection, treatment strategies, and treatment location affect overall survival. The present study highlights factors that affect patient survival after surgery in British Columbia. Our data are gathered within a single-payer, high-resource setting which removes possible confounders in outcome analysis. We find persistent differences in overall survival when compared with clinical trials and the Surveillance, Epidemiology, and End Results database. Further efforts should ensure access to the gold standard of care. All neuro-oncology centers should analyze the real-world outcomes of their local glioblastoma treatment strategies. Knowledge of the variance from expected and comparative results are fundamental for appropriate patient care.

Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition.

Although interspecific competition plays a principal role in shaping species behaviour and demography, little is known about the population-level outcomes of competition between large carnivores, and the mechanisms that facilitate coexistence. We conducted a multilandscape analysis of two widely distributed, threatened large carnivore competitors to offer insight into coexistence strategies and assist with species-level conservation. We evaluated how interference competition affects occupancy, temporal activity and population density of a dominant competitor, the lion (Panthera leo), and its subordinate competitor, the leopard (Panthera pardus). We collected camera-trap data over 3years in 10 study sites covering 5,070km2 . We used multispecies occupancy modelling to assess spatial responses in varying environmental and prey conditions and competitor presence, and examined temporal overlap and the relationship between lion and leopard densities across sites and years. Results showed that both lion and leopard occupancy was independent of-rather than conditional on-their competitor's presence across all environmental covariates. Marginal occupancy probability for leopard was higher in areas with more bushy, "hideable" habitat, human (tourist) activity and topographic ruggedness, whereas lion occupancy decreased with increasing hideable habitat and increased with higher abundance of very large prey. Temporal overlap was high between carnivores, and there was no detectable relationship between species densities. Lions pose a threat to the survival of individual leopards, but they exerted no tractable influence on leopard spatial or temporal dynamics. Furthermore, lions did not appear to suppress leopard populations, suggesting that intraguild competitors can coexist in the same areas without population decline. Aligned conservation strategies that promote functioning ecosystems, rather than target individual species, are therefore advised to achieve cost- and space-effective conservation.

Do soil biota influence the outcome of novel interactions between plant competitors?

1. Species are shifting their ranges, for example to higher elevations, in response to climate change. Different plant species and soil microbiota will likely shift their ranges at different rates, giving rise to novel communities of plants and soil organisms. However, the ecological consequences of such novel plant-soil interactions are poorly understood. We experimentally simulated scenarios for novel interactions arising between high- and low elevation plants and soil biota following asynchronous climate change range shifts, asking to what extent the ability of plants to coexist depends on the origin of the soil biota. 2. In a greenhouse experiment, we grew pairs of low- (Poa trivialis and Plantago lanceolata) and high- (Poa alpina and Plantago alpina) elevation plant species alone and against a density gradient of con- or heterospecific neighbours. Plants grew on sterilized field soil that was inoculated with a soil community sampled from either low- or high elevation in the western Swiss Alps. We used the experiment to parameterize competition models, from which we predicted the population-level outcomes of competition in the presence of the different soil biota. 3. In the absence of neighbours, three of the four species produced more biomass with the low elevation soil biota. As a result of generally similar responses across plant species, soil biota tended not to affect plant interaction outcomes, with the low elevation species generally predicted to competitively exclude high elevation species irrespective of the soil biota origin. However, the low elevation grass Poa trivialis was only able to invade communities of Poa alpina in the presence of a low elevation soil biota. This suggests that, at least in some cases, the outcome of novel competitive interactions between plants following climate change will depend on whether shifts in the distribution of plant and soil organisms are asynchronous. 4. Synthesis. Our results indicate that the changing soil communities that plants encounter during range expansion can influence plant performance. However, this is only likely to alter expectations for the ability of plants to coexist following climate change if plant species respond differently to the change in the soil community.

Spatial Moment Description of Birth-Death-Movement Processes Incorporating the Effects of Crowding and Obstacles.

Birth-death-movement processes, modulated by interactions between individuals, are fundamental to many cell biology processes. A key feature of the movement of cells within in vivo environments is the interactions between motile cells and stationary obstacles. Here we propose a multi-species model of individual-level motility, proliferation and death. This model is a spatial birth-death-movement stochastic process, a class of individual-based model (IBM) that is amenable to mathematical analysis. We present the IBM in a general multi-species framework and then focus on the case of a population of motile, proliferative agents in an environment populated by stationary, non-proliferative obstacles. To analyse the IBM, we derive a system of spatial moment equations governing the evolution of the density of agents and the density of pairs of agents. This approach avoids making the usual mean-field assumption so that our models can be used to study the formation of spatial structure, such as clustering and aggregation, and to understand how spatial structure influences population-level outcomes. Overall the spatial moment model provides a reasonably accurate prediction of the system dynamics, including important effects such as how varying the properties of the obstacles leads to different spatial patterns in the population of agents.

Individual preventive social distancing during an epidemic may have negative population-level outcomes.

The outbreak of an infectious disease in a human population can lead to individuals responding with preventive measures in an attempt to avoid getting infected. This leads to changes in contact patterns. However, as we show in this paper, rational behaviour at the individual level, such as social distancing from infectious contacts, may not always be beneficial for the population as a whole. We use epidemic network models to demonstrate the potential negative consequences at the population level. We take into account the social structure of the population through several network models. As the epidemic evolves, susceptible individuals may distance themselves from their infectious contacts. Some individuals replace their lost social connections by seeking new ties. If social distancing occurs at a high rate at the beginning of an epidemic, then this can prevent an outbreak from occurring. However, we show that moderate social distancing can worsen the disease outcome, both in the initial phase of an outbreak and the final epidemic size. Moreover, the same negative effect can arise in real-world networks. Our results suggest that one needs to be careful when targeting behavioural changes as they could potentially worsen the epidemic outcome. Furthermore, network structure crucially influences the way that individual-level measures impact the epidemic at the population level. These findings highlight the importance of careful analysis of preventive measures in epidemic models.

Intrinsic and instrumental perspectives to sanitation

Public health interventions are often implemented because they are a means to an end. For example, improving population-level health outcomes is a key step towards improving social and economic outcomes, too. But what is often overlooked is the fact that a given public health intervention might be the end in itself. In other words, a given intervention might be worth investing in even if there are zero returns from investing in it. This intrinsic value, however, is often overlooked. In this commentary, we look specifically at sanitation, and why the development community should motivate sanitation interventions using an intrinsic value perspective. We also extend the conversation to why there needs to be a fundamental shift away from demand-side interventions to supply-side interventions. In doing so, we hope to offer a more equitable perspective to health and development.

Effect of routing paradigm on patient-centered outcomes in acute ischemic stroke

This article has been retracted

Chemical-Induced Phenotypes at CTD Help Inform the Predisease State and Construct Adverse Outcome Pathways.

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org) is a public resource that manually curates the scientific literature to provide content that illuminates the molecular mechanisms by which environmental exposures affect human health. We introduce our new chemical-phenotype module that describes how chemicals can affect molecular, cellular, and physiological phenotypes. At CTD, we operationally distinguish between phenotypes and diseases, wherein a phenotype refers to a nondisease biological event: eg, decreased cell cycle arrest (phenotype) versus liver cancer (disease), increased fat cell proliferation (phenotype) versus morbid obesity (disease), etc. Chemical-phenotype interactions are expressed in a formal structured notation using controlled terms for chemicals, phenotypes, taxon, and anatomical descriptors. Combining this information with CTD's chemical-disease module allows inferences to be made between phenotypes and diseases, yielding potential insight into the predisease state. Integration of all 4 CTD modules furnishes unique opportunities for toxicologists to generate computationally predictive adverse outcome pathways, linking chemical-gene molecular initiating events with phenotypic key events, adverse diseases, and population-level health outcomes. As examples, we present 3 diverse case studies discerning the effect of vehicle emissions on altered leukocyte migration, the role of cadmium in influencing phenotypes preceding Alzheimer disease, and the connection of arsenic-induced glucose metabolic phenotypes with diabetes. To date, CTD contains over 165000 interactions that connect more than 6400 chemicals to 3900 phenotypes for 760 anatomical terms in 215 species, from over 19000 scientific articles. To our knowledge, this is the first comprehensive set of manually curated, literature-based, contextualized, chemical-induced, nondisease phenotype data provided to the public.

A Research Program on Implementing Integrated Care for Older Adults with Complex Health Needs (iCOACH): An International Collaboration.

Health and social care systems across western developed nations are being challenged to meet the needs of an increasing number of people aging with multiple complex health and social needs. Community based primary health care (CBPHC) has been associated with more equitable access to services, better population level outcomes and lower system level costs. Itmay be well suited to the increasingly complex needs of populations; however the implementation of CBPHC models of care faces many challenges. This paper describes a program of research by an international, multi-university, multidisciplinary research team who are seeking to understand how to scale up and spread models of Integrated CBPHC (ICBPHC). The key question being addressed is “What are the steps to implementing innovative integrated community-based primary health care models that address the health and social needs of older adults with complex care needs?” and will be answered in three phases. In the first phase we identify and describe exemplar models of ICBPHC and their context in relation to relevant policies and performance across the three jurisdictions (New Zealand, Ontario and Québec, Canada). The second phase involves a series of theory-informed, mixed methods case studies from which we shall develop a conceptual framework that captures not only the attributes of successful innovative ICBPHC models, but also how these models are being implemented. In the third phase, we aim to translate our research into practice by identifying emerging models of ICBPHC in advance, and working alongside policymakers to inform the development and implementation of these models in each jurisdiction. The final output of the program will be a comprehensive guide to the design, implementation and scaling-up of innovative models of ICBPHC.

Population-level outcomes of early versus delayed appendectomy for acute appendicitis using the American College of Surgeons National Surgical Quality Improvement Program

BackgroundThe optimal timing of appendectomy for acute appendicitis has been analyzed with mixed results. We hypothesized that delayed appendectomy would be associated with increased 30-d morbidity and mortality. Materials and methodsThe American College of Surgeons National Surgical Quality Improvement Program database was queried for all patients undergoing nonelective appendectomy from 2012 to 2015 with a postoperative diagnosis of appendicitis. Patients were grouped based on hospital day (HD) of operation. Primary outcomes included 30-d mortality and major complications. Logistic regression was performed to determine predictors of major morbidity and mortality. ResultsFrom 2012 to 2015, 112,122 patients underwent appendectomy for acute appendicitis. Appendectomies performed on HD 3 had significantly worse outcomes as demonstrated by increased 30-d mortality (0.6%) and all major postoperative complications (8%) in comparison with operations taking place on HD 1 (0.1%; 3.4%) or HD 2 (0.1%, P < 0.001; 3.6%, P < 0.001). In subgroup analysis, open operations had significantly higher mortality and major postoperative complications, including organ/space surgical site infections (4.6% open versus 2.1% laparoscopic; P < 0.001). Patients with decreased baseline physical status by the American Society of Anesthesiologists Physical Status class had the worst outcomes (1.5% mortality; 14% major complications) when operation was delayed to HD 3. Logistic regression revealed higher American Society of Anesthesiologists Physical Status class and open operations as predictors of major complications; however, HD was not (P = 0.2). ConclusionsData from the American College of Surgeons National Surgical Quality Improvement Program demonstrate similar outcomes of appendectomy for acute appendicitis when the operation is performed on HD 1 or 2; however, outcomes are significantly worse for appendectomies delayed until HD 3. Increased complications in this group are likely not attributable to HD of operation, but rather decreased baseline health status and procedure type.

0411 Trends in Insomnia Prescription MedicTrends in Insomnia Prescription Medication Use Among Medicare Beneficiaries, 2006–2013

Traditional pharmacotherapy for insomnia has known adverse outcomes among older adults, including falls and cognitive impairment. However, few studies have characterized longitudinal insomnia-related medication trends among Medicare beneficiaries. These characteristics are of particular interest given the recent (2012) inclusion of benzodiazepines in the Medicare prescription drug formulary. The objective of this study was to characterize trends in insomnia-related prescription medication use among Medicare beneficiaries over a seven-year period. We conducted a time-series analysis to estimate the annual prevalence of use of insomnia medications using a 5% sample of Medicare fee-for-service beneficiaries from 2006–2013. Insomnia medication use (defined as at least 1 fill annually) was identified by searching the Part D prescription drug claims and included insomnia-related medication classes and drugs: barbiturates, benzodiazepines, chloral hydrate, hydroxyzine, non-benzodiazepine sedative hypnotics (NBSH), and sedating anti-depressants. Prevalence of insomnia medication use ranged from 20.8% in 2006 to 23.4% in 2013 (p>.10). Benzodiazepine use increased from 0.5% in 2012 to 8.0% in 2013 (p = 0.03). Use of sedating antidepressants and NBSH decreased modestly from 9% and 8%, respectively, in 2006 through 2013. Prevalence of sedating antidepressant, NBSH, and hydroxyzine use was higher among women than men (p<0.001 for all). Benzodiazepine and sedating antidepressant use was most common among those age <65 and ≥85 years. NBSH use was most common among those age <65 years. Use of all drugs was similar among those ages 65 - 74 years and 75 - 84 years. Prevalence of insomnia medication use remained high among Medicare beneficiaries from 2006 - 2013, particularly among women. Benzodiazepine use increased sharply following inclusion in the Medicare formulary. Future studies should examine population-level outcomes associated with this increased use of benzodiazepines. Merck Investigator Initiated Studies Program (PI: Wickwire).

Bribery games on interdependent complex networks

Bribe demands present a social conflict scenario where decisions have wide-ranging economic and ethical consequences. Nevertheless, such incidents occur daily in many countries across the globe. Harassment bribery constitute a significant sub-set of such bribery incidents where a government official demands a bribe for providing a service to a citizen legally entitled to it. We employ an evolutionary game-theoretic framework to analyse the evolution of corrupt and honest strategies in structured populations characterized by an interdependent complex network. The effects of changing network topology, average number of links and asymmetry in size of the citizen and officer population on the proliferation of incidents of bribery are explored. A complex network topology is found to be beneficial for the dominance of corrupt strategies over a larger region of phase space when compared with the outcome for a regular network, for equal citizen and officer population sizes. However, the extent of the advantage depends critically on the network degree and topology. A different trend is observed when there is a difference between the citizen and officer population sizes. Under those circumstances, increasing randomness of the underlying citizen network can be beneficial to the fixation of honest officers up to a certain value of the network degree. Our analysis reveals how the interplay between network topology, connectivity and strategy update rules can affect population level outcomes in such asymmetric games.

Population-level incidence and outcomes of myocardial infarction with non-obstructive coronary arteries (MINOCA): Insights from the Alberta contemporary acute coronary syndrome patients invasive treatment strategies (COAPT) study

BackgroundMyocardial infarction with non-obstructive coronary arteries (MINOCA) is a known clinical conundrum with limited investigation. Using a large population-based cohort, we examined the incidence, demographic profile, use of evidence-based medicines (EBM) and clinical outcomes of MINOCA patients. MethodsPatients hospitalized with a primary diagnosis of MI who underwent coronary angiography between 01/04/2002 and 31/03/2014 in Alberta, Canada, were included in the study. Comparisons were made between patients with MINOCA versus obstructive coronary disease (OCD). The primary composite endpoint was 1-year all-cause death or re-MI. ResultsOf 35,928 patients hospitalized with MI, 2092 (5.8%) had MINOCA. In-hospital mortality rate was 0.8% among MINOCA, and 2.7% among patients with OCD (p < 0.0001). At 6 months, cardiovascular EBM rates were significantly lower among MINOCA patients compared to OCD patients. One-year death/re-MI rate was 5.3% in MINOCA and 8.9% in patients with OCD (adjusted hazard ratio (AHR) 0.75, 95% confidence interval (CI) 0.62–0.92, p < 0.0001). Five-year mortality rates were 10.9% in MINOCA and 16.0% in patients with OCD. Upon further stratification, 770 (36.8%) of MINOCA patients had no angiographic evidence of CAD (i.e. normal angiograms). EBM rates were even lower among these patients. One-year death/re-MI rate among these patients was 3.9% as compared to 6.1% among MINOCA patients with stenosis <50% (AHR 0.68, 95% CI 0.44–1.07, p = 0.028). ConclusionsThe population-level incidence of MINOCA is approximately 5%. Despite their apparently benign anatomic findings, efforts must be made to improve secondary prevention strategies to reduce the burden of long-term adverse outcomes in this population.

Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.

The Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Working Group is a consortium of National Cancer Institute-sponsored investigators who use statistical and simulation modeling to evaluate the impact of cancer control interventions on long-term population-level breast cancer outcomes such as incidence and mortality and to determine the impact of different breast cancer control strategies. The CISNET breast cancer models have been continuously funded since 2000. The models have gone through several updates since their inception to reflect advances in the understanding of the molecular basis of breast cancer, changes in the prevalence of common risk factors, and improvements in therapy and early detection technology. This article provides an overview and history of the CISNET breast cancer models, provides an overview of the major changes in the model inputs over time, and presents examples for how CISNET breast cancer models have been used for policy evaluation.

Can Family Planning Service Statistics Be Used to Track Population-Level Outcomes?

The need for annual family planning program tracking data under the Family Planning 2020 (FP2020) initiative has contributed to renewed interest in family planning service statistics as a potential data source for annual estimates of the modern contraceptive prevalence rate (mCPR). We sought to assess (1) how well a set of commonly recorded data elements in routine service statistics systems could, with some fairly simple adjustments, track key population-level outcome indicators, and (2) whether some data elements performed better than others. We used data from 22 countries in Africa and Asia to analyze 3 data elements collected from service statistics: (1) number of contraceptive commodities distributed to clients, (2) number of family planning service visits, and (3) number of current contraceptive users. Data quality was assessed via analysis of mean square errors, using the United Nations Population Division World Contraceptive Use annual mCPR estimates as the "gold standard." We also examined the magnitude of several components of measurement error: (1) variance, (2) level bias, and (3) slope (or trend) bias. Our results indicate modest levels of tracking error for data on commodities to clients (7%) and service visits (10%), and somewhat higher error rates for data on current users (19%). Variance and slope bias were relatively small for all data elements. Level bias was by far the largest contributor to tracking error. Paired comparisons of data elements in countries that collected at least 2 of the 3 data elements indicated a modest advantage of data on commodities to clients. None of the data elements considered was sufficiently accurate to be used to produce reliable stand-alone annual estimates of mCPR. However, the relatively low levels of variance and slope bias indicate that trends calculated from these 3 data elements can be productively used in conjunction with the Family Planning Estimation Tool (FPET) currently used to produce annual mCPR tracking estimates for FP2020.

Do I walk or ride the rickshaw? Examining the factors affecting first- and last-mile trip options in the historic district of Manila (Philippines)

Historic urban centers (HUCs) such as the Ermita District in Manila display a compact, mixed, and human-scale urban form. Because of these features, people in these areas still depend on either walking or riding a pedicab (also known as cycle rickshaws) to reach their destinations. The latter mode, considered an informal non-motorized transport (NMT), is widely preferred by commuters as their first- and last-mile trip option to navigate the narrow street network of these historic districts. However, it is unclear what factors affect an individual’s first- and last-mile choices. Through a face-to-face intercept survey, respondents were asked about their relative preference between the two mode choices to capture the factors that influenced their decision to walk or to ride the pedicab within Ermita. By utilizing logit choice analysis, the study identified statistically significant mode-specific, as well as qualitative, variables that influenced individual decisions. The probability outcome showed that the most significant factors were access and/or egress time, cost over travel time, safety, and accessibility of the walking environment. It is also important to note that pedicab users had a longer average trip distance (about a kilometer) than walkers, and women, including those who were accompanied by children, preferred to use pedicabs. Results from this study can help district-level planning and policymaking in three ways: (1) by improving the physical environment through encouraging the use of NMT such as walking and pedicab riding as crucial first- and/or last-mile options for individuals in HUCs; (2) by aligning routes and regulations for pedicab services to be part of an overall transport service provision, and (3) by undertaking infrastructure improvements for safer walkway environments for pedestrians, considering the implications of walking and pedicab riding to individual, population-level health outcomes and overall quality of life.