Rationale: The current hypothesis that human pulmonary alveolarization is complete by 3 years is contradicted by new evidence of alveolarization throughout adolescence in mammals.

Objectives: We reexamined the current hypothesis using helium-3 (3He) magnetic resonance (MR) to assess alveolar size noninvasively between 7 and 21 years, during which lung volume nearly quadruples. If new alveolarization does not occur, alveolar size should increase to the same extent.

Methods: Lung volumes were measured by spirometry and plethysmography in 109 healthy subjects aged 7–21 years. Using 3HeMR we determined two independent measures of peripheral airspace dimensions: apparent diffusion coefficient (ADC) of 3He at FRC (n = 109), and average diffusion distance of helium (Xrms¯) by q-space analysis (n = 46). We compared the change in these parameters with lung growth against a model of lung expansion with no new alveolarization.

Measurements and Main Results: ADC increased by 0.19% for every 1% increment in FRC (95% confidence interval [CI], 0.13–0.25), whereas the expected change in the absence of neoalveolarization is 0.41% (95% CI, 0.31–0.52). Similarly, increase of (Xrms¯) with FRC was significantly less than the predicted increase in the absence of neoalveolarization. The number of alveoli is estimated to increase 1.94-fold (95% CI, 1.64–2.30) across the age range studied.

Conclusions: Our observations are best explained by postulating that the lungs grow partly by neoalveolarization throughout childhood and adolescence. This has important implications: developing lungs have the potential to recover from early life insults and respond to emerging alveolar therapies. Conversely, drugs, diseases, or environmental exposures could adversely affect alveolarization throughout childhood.

Background Previous studies on childhood cancer and nuclear power plants (NPPs) produced conflicting results. We used a cohort approach to examine whether residence near NPPs was associated with leukaemia or any childhood cancer in Switzerland.

Methods We computed person-years at risk for children aged 0–15 years born in Switzerland from 1985 to 2009, based on the Swiss censuses 1990 and 2000 and identified cancer cases from the Swiss Childhood Cancer Registry. We geo-coded place of residence at birth and calculated incidence rate ratios (IRRs) with 95% confidence intervals (CIs) comparing the risk of cancer in children born <5 km, 5–10 km and 10–15 km from the nearest NPP with children born >15 km away, using Poisson regression models.

Results We included 2925 children diagnosed with cancer during 21 117 524 person-years of follow-up; 953 (32.6%) had leukaemia. Eight and 12 children diagnosed with leukaemia at ages 0–4 and 0–15 years, and 18 and 31 children diagnosed with any cancer were born <5 km from a NPP. Compared with children born >15 km away, the IRRs (95% CI) for leukaemia in 0–4 and 0–15 year olds were 1.20 (0.60–2.41) and 1.05 (0.60–1.86), respectively. For any cancer, corresponding IRRs were 0.97 (0.61–1.54) and 0.89 (0.63–1.27). There was no evidence of a dose–response relationship with distance (P > 0.30). Results were similar for residence at diagnosis and at birth, and when adjusted for potential confounders. Results from sensitivity analyses were consistent with main results.

Conclusions This nationwide cohort study found little evidence of an association between residence near NPPs and the risk of leukaemia or any childhood cancer.

Matthias Egger and colleagues present a nomogram and a web-based calculator to correct estimates of program-level mortality for loss to follow-up, for use in antiretroviral treatment programs.

Background

The World Health Organization estimates that in sub-Saharan Africa about 4 million HIV-infected patients had started antiretroviral therapy (ART) by the end of 2008. Loss of patients to follow-up and care is an important problem for treatment programmes in this region. As mortality is high in these patients compared to patients remaining in care, ART programmes with high rates of loss to follow-up may substantially underestimate mortality of all patients starting ART.

Methods and Findings

We developed a nomogram to correct mortality estimates for loss to follow-up, based on the fact that mortality of all patients starting ART in a treatment programme is a weighted average of mortality among patients lost to follow-up and patients remaining in care. The nomogram gives a correction factor based on the percentage of patients lost to follow-up at a given point in time, and the estimated ratio of mortality between patients lost and not lost to follow-up. The mortality observed among patients retained in care is then multiplied by the correction factor to obtain an estimate of programme-level mortality that takes all deaths into account. A web calculator directly calculates the corrected, programme-level mortality with 95% confidence intervals (CIs). We applied the method to 11 ART programmes in sub-Saharan Africa. Patients retained in care had a mortality at 1 year of 1.4% to 12.0%; loss to follow-up ranged from 2.8% to 28.7%; and the correction factor from 1.2 to 8.0. The absolute difference between uncorrected and corrected mortality at 1 year ranged from 1.6% to 9.8%, and was above 5% in four programmes. The largest difference in mortality was in a programme with 28.7% of patients lost to follow-up at 1 year.

Conclusions

The amount of bias in mortality estimates can be large in ART programmes with substantial loss to follow-up. Programmes should routinely report mortality among patients retained in care and the proportion of patients lost. A simple nomogram can then be used to estimate mortality among all patients who started ART, for a range of plausible mortality rates among patients lost to follow-up.

Please see later in the article for the Editors' Summary

Editors' Summary

Background

AIDS has killed more than 25 million people since 1981 and about 33 million people (30 million of them in low- and middle-income countries) are now infected with HIV, which causes AIDS. HIV destroys immune system cells, leaving infected individuals susceptible to other infections. Early in the AIDS epidemic, most HIV-infected people died within 10 years of infection. Then, in 1996, highly active antiretroviral therapy (ART) became available. For people living in affluent, developed countries, HIV/AIDS became a chronic condition, but for people living in low- and middle-income countries, ART was prohibitively expensive and HIV/AIDS remained a fatal illness. In 2003, this situation was declared a global health emergency and governments, international agencies, and funding bodies began to implement plans to increase ART coverage in developing countries. By the end of 2009, 5.25 million of the 14.6 million people in low- and middle-income countries who needed ART (36%) were receiving it.

Why Was This Study Done?

ART program managers in developing countries need to monitor the effectiveness of their programs to ensure that their limited resources are used wisely. In particular, they need accurate records of the death (mortality) rates in their programs. However, in resource-limited countries, many patients drop out of ART programs. In sub-Saharan Africa, for example, only about 60% of patients are retained in ART programs 2 years after starting therapy. In many programs, it is not known how many of the patients lost to follow-up subsequently die, but it is known that mortality is higher among these patients than among those who remain in care. Thus, in programs with high dropout rates and poor ascertainment of death in patients lost to follow-up, estimates of the mortality of all patients starting ART are underestimates. In this study, the researchers develop a simple nomogram (a graphical method for finding the value of a third variable from the values of two other variables) to correct estimates of program-level mortality for loss to follow-up.

What Did the Researchers Do and Find?

The researchers' nomogram uses the percentage of patients lost to follow and the estimated ratio of mortality between patients lost and not lost to follow-up to provide a correction factor that converts mortality among patients remaining in care to mortality among all the patients in a program. The researchers first applied their nomogram to the Academic Model Providing Access to Healthcare (AMPATH), a large ART program in Kenya. They used data collected by outreach teams to estimate mortality among the 40.5% of patients lost to follow-up at two AMPATH sites between 1 January 2005 and 31 January 2007. The uncorrected estimate of mortality over this period was 2.8%, whereas the corrected estimate obtained using the nomogram was 9.4%. The researchers then applied their nomogram to 11 other African ART programs. This time, the researchers used a statistical model to provide estimates of mortality among patients lost to follow-up. Mortality among patients retained in care was 1.4% to 12.0% at 1 year; loss to follow-up ranged from 2.8% to 28.7%. The nomogram provided a correction value for mortality among all patients in the ART program of 1.2 to 8.0, which resulted in absolute differences between uncorrected and corrected mortality of 1.6% to 9.8%. The largest absolute difference was in the program with the largest percentage of patients lost to follow-up.

What Do These Findings Mean?

These findings indicate that, in ART programs where a large percentage of patients are lost to follow-up, program-level mortality estimates based on the mortality among patients retained in the program can be substantial underestimates. This bias needs to be taken into account when comparing the effectiveness of different programs, so the researchers recommend that all programs routinely report mortality among patients retained in care and the proportion of patients lost to follow-up. The nomogram developed by the researchers can then be used to estimate mortality among all patients who started ART using a range of plausible mortality rates among patients lost to follow-up. To help program managers make use of the nomogram, the researchers provide a user-friendly web calculator based on the nomogram on the International epidemiologic Databases to Evaluate AIDS (IeDEA) Southern Africa website.

Additional Information

Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1000390.

This study is further discussed in a PLoS Medicine Perspective by Gregory Bisson

Information is available from the US National Institute of Allergy and Infectious Diseases on HIV infection and AIDS

HIV InSite has comprehensive information on all aspects of HIV/AIDS

Information is available from Avert, an international AIDS charity on many aspects of HIV/AIDS, including information on the HIV and AIDS in Africa, and on universal access to AIDS treatment (in English and Spanish)

The World Health Organization provides information about universal access to AIDS treatment, including the 2010 progress report (in English, French and Spanish)

The International epidemiologic Databases to Evaluate Aids (IeDEA) Southern Africa website provides access to a calculator for correcting overall program-specific mortality for loss to follow-up

Background

Evaluation of antiretroviral treatment (ART) programmes in sub-Saharan Africa is difficult because many patients are lost to follow-up. Outcomes in these patients are generally unknown but studies tracing patients have shown mortality to be high. We adjusted programme-level mortality in the first year of antiretroviral treatment (ART) for excess mortality in patients lost to follow-up.

Methods and Findings

Treatment-naïve patients starting combination ART in five programmes in Côte d'Ivoire, Kenya, Malawi and South Africa were eligible. Patients whose last visit was at least nine months before the closure of the database were considered lost to follow-up. We filled missing survival times in these patients by multiple imputation, using estimates of mortality from studies that traced patients lost to follow-up. Data were analyzed using Weibull models, adjusting for age, sex, ART regimen, CD4 cell count, clinical stage and treatment programme. A total of 15,915 HIV-infected patients (median CD4 cell count 110 cells/µL, median age 35 years, 68% female) were included; 1,001 (6.3%) were known to have died and 1,285 (14.3%) were lost to follow-up in the first year of ART. Crude estimates of mortality at one year ranged from 5.7% (95% CI 4.9–6.5%) to 10.9% (9.6–12.4%) across the five programmes. Estimated mortality hazard ratios comparing patients lost to follow-up with those remaining in care ranged from 6 to 23. Adjusted estimates based on these hazard ratios ranged from 10.2% (8.9–11.6%) to 16.9% (15.0–19.1%), with relative increases in mortality ranging from 27% to 73% across programmes.

Conclusions

Naïve survival analysis ignoring excess mortality in patients lost to follow-up may greatly underestimate overall mortality, and bias ART programme evaluations. Adjusted mortality estimates can be obtained based on excess mortality rates in patients lost to follow-up.

Background

Wheezing disorders in childhood vary widely in clinical presentation and disease course. During the last years, several ways to classify wheezing children into different disease phenotypes have been proposed and are increasingly used for clinical guidance, but validation of these hypothetical entities is difficult.

Methodology/Principal Findings

The aim of this study was to develop a testable disease model which reflects the full spectrum of wheezing illness in preschool children. We performed a qualitative study among a panel of 7 experienced clinicians from 4 European countries working in primary, secondary and tertiary paediatric care. In a series of questionnaire surveys and structured discussions, we found a general consensus that preschool wheezing disorders consist of several phenotypes, with a great heterogeneity of specific disease concepts between clinicians. Initially, 24 disease entities were described among the 7 physicians. In structured discussions, these could be narrowed down to three entities which were linked to proposed mechanisms: a) allergic wheeze, b) non-allergic wheeze due to structural airway narrowing and c) non-allergic wheeze due to increased immune response to viral infections. This disease model will serve to create an artificial dataset that allows the validation of data-driven multidimensional methods, such as cluster analysis, which have been proposed for identification of wheezing phenotypes in children.

Conclusions/Significance

While there appears to be wide agreement among clinicians that wheezing disorders consist of several diseases, there is less agreement regarding their number and nature. A great diversity of disease concepts exist but a unified phenotype classification reflecting underlying disease mechanisms is lacking. We propose a disease model which may help guide future research so that proposed mechanisms are measured at the right time and their role in disease heterogeneity can be studied.