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Quality-adjusted life years are used in cost-effectiveness analyses (CEAs). To calculate QALYs, a "utility" (0-1) is used for each health state induced or prevented by the intervention. We aimed to estimate the impact of quality of life (QoL) assumptions (utilities and durations of health states) on CEAs of cervical cancer screening. To do so, 12 alternative sets of utility assumptions were retrieved from published cervical cancer screening CEAs. Two additional sets were based on empirical QoL data that were integrally obtained through two different measures (SF-6D and EQ-5D) from eight groups of women (total n = 3,087), from invitation for screening to diagnosis with cervical cancer. Per utility set we calculated the number of quality-adjusted days lost (QADL) for each relevant health state in cervical cancer screening, by multiplying the study-specific assumed disutilities (i.e., 1-utility) with study-specific durations of the loss in QoL, resulting in 14 "QADL-sets." With microsimulation model MISCAN we calculated cost-effectiveness of 342 alternative screening programs (varying in primary screening test [Human Papillomavirus (HPV) vs. cytology], starting ages, and screening interval) for each of the 14 QADL-sets. Utilities used in CEAs appeared to differ largely. We found that ten QADL-sets from the literature resulted in HPV and two in cytology as preferred primary test. The SF-6D empirical QADL-set resulted in cytology and the EQ-5D one in HPV as preferred primary test. In conclusion, assumed utilities and health state durations determine cost-effectiveness of cervical cancer screening. Also, the measure used to empirically assess utilities can be crucial for CEA conclusions.

One of the aims in reproductive medicine is to differentiate between couples that have favourable chances of conceiving naturally and those that do not. Since the development of the prediction model of Hunault, characteristics of the subfertile population have changed. The objective of this analysis was to assess whether additional predictors can refine the Hunault model and extend its applicability. Consecutive subfertile couples with unexplained and mild male subfertility presenting in fertility clinics were asked to participate in a prospective cohort study. We constructed a multivariable prediction model with the predictors from the Hunault model and new potential predictors. The primary outcome, natural conception leading to an ongoing pregnancy, was observed in 1053 women of the 5184 included couples 120%). All predictors of the Hunault model were selected into the revised model plus an additional seven Ivvoman's body mass index, cycle length, basal FSH levels, tubal status, history of previous pregnancies in the current relationship [ongoing pregnancies after natural conception, fertility treatment or miscarriages), semen volume, and semen morphology. Predictions from the revised model seem to concur better with observed pregnancy rates compared with the Hunault model; c-statistic of 0.71 195% CI 0.69 to 0.731 compared with 0.59 195% CI 0.57 to 0.61). (C) 2017 Published by Elsevier Ltd on behalf of Reproductive Healthcare Ltd

The intervals between screens for the early detection of diseases such as breast and colon cancer suggested by screening guidelines are typically based on the average population risk of disease. With the emergence of ever more biomarkers for cancer risk prediction and the development of personalized medicine, there is a need for risk-specific screening intervals. The interval between successive screens should be shorter with increasing cancer risk. A risk-dependent optimal interval is ideally derived from a cost-effectiveness analysis using a validated simulation model. However, this is time-consuming and costly. We propose a simplified mathematical approach for the exploratory analysis of the implications of risk level on optimal screening interval. We develop a mathematical model of the optimal screening interval for breast cancer screening. We verified the results by programming the simplified model in the MISCAN-Breast microsimulation model and comparing the results. We validated the results by comparing them with the results of a full, published MISCAN-Breast cost-effectiveness model for a number of different risk levels. The results of both the verification and validation were satisfactory. We conclude that the mathematical approach can indicate the impact of disease risk on the optimal screening interval.

Reliable methods of contraception were introduced in the1960s and enabled women to postpone childbearing and to prevent the birth of not-yet-wanted children. This allowed women to delay the start of their family. However, with increasing female age (>30 years and especially >35 years), there is increased risk of aneuploidy and either miscarriage or a deformed baby. The availability of in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) methods allows embryo selection prior to implantation in these women and birth of healthy babies. This allows older women to delay their first pregnancy and plan to have more than 1 child. As a result, the mean age of women at first childbirth increased by 4 to 5 years since the 1970s in most European countries, and the proportion who have their first child at older than 30 years has increased from about 8% to 40%. Both the treatment-independent and treatment-dependent (IVF/ICSI) chances of pregnancy decline with female age. Research on the age-dependent effects has focused on the chance of a first pregnancy and not on having more than 1 child. This study was designed to estimate at what age couples can wait to start a family without compromising their chances of realizing the desired number of children (a 1-, 2-, or 3-child family). The investigators used an established computer simulation model of fertility to simulate a cohort of 10,000 couples in order to assess the chances of realizing a 1-, 2-, or 3-child family. The model used treatment-independent pregnancy chances and pregnancy chances after IVF/ICSI updated with recent IVF success rates. Three levels of importance a couple attaches to achieving a desired family size were singled out: (i) The extremely important level is aimed for at least a 90% success chance; (ii) important (but not at all costs) is aimed at a 75% success chance; and (iii) good to have children (parenthood is not essential) is aimed at a 50% success chance. Based on the computer model, couples who wish to have at least a 90% chance to realize a 1-child family, the age for the female partner to start trying to conceive using IVF should be 35 years or younger. For those desiring 2 children using IVF, the latest starting age should be 31 years, and for 3 children, 28 years. Without IVF, couples who desire a 90% chance should start no later than age 32 years for a 1-child family, at 27 years for a 2-child family, and at 23 years for 3 children. If a 75% or lower chance is acceptable for couples attempting to conceive naturally, they can start 4 to 11 years later for a 1-, 2-, or 3-child family (at 37, 34, and 31 years of age, respectively). Use of IVF has the greatest impact on the starting age for the couples wishing a 90% chance of conceiving. For a 50% or 75% chance at older ages of realizing a 2- or 3-child family, there was little improvement for couples using IVF. The data appeared to be robust for plausible changes in model assumptions. The latest female age at which a couple should start trying to become pregnant is strongly dependent on the importance they attach to achieving a desired family size and on whether IVF is an acceptable option if no natural pregnancy occurs. In the absence of large-scale prospective studies (which would provide more conclusive data), this evidence-based simulation study is the next best option. With continuing improvements in IVF technology, the assumptions on success chances with IVF run the risk of becoming outdated. Therefore, simulations should be updated every 5 to 10 years with new evidence. These findings are important for prospective parents in family planning and for preconception counseling. This information is also useful for inclusion in educational courses in human biology and for increasing public awareness on possibilities and limitations of human reproduction.

OBJECTIVE: To assess the effect of age at initiation and interval of cervical cancer screening in women of reproductive age on the risk of future preterm birth and subsequent adverse neonatal outcome relative to maternal life-years gained and cost of both screening and preterm birth.METHODS: In this decision and cost-effectiveness analysis, we compared eight cytology-based screening programs varying in age of onset (21, 24, 25, 27, or 30 years) and screening interval (3 or 5 years) in a fictive cohort of 100,000 women. We used the microsimulation screening analysis model to estimate number of cervical intraepithelial neoplasia diagnoses, large loop excisions of the transformation zone (LLETZs), life-years gained, cervical cancer cases, deaths, and costs of screening and treatment. We used the number of LLETZs to calculate additional preterm births, subsequent neonatal morbidity, mortality, and associated costs.RESULTS: The number of LLETZs per 100,000 women varied from 9,612 for the most intensive screening (every 3 years from age 21 years) to 4,646 for the least intensive screening (every 5 years from age 30 years). Compared with the least intensive program, the most intensive program increased maternal life-years gained by 9% (10,728 compared with 9,839), decreased cervical cancer cases by 67% (52 compared with 158), and cervical cancer deaths by 75% (four compared with 16) at the expense of 250% (158 compared with 45) more preterm births and 320% (four compared with one) more neonatal deaths while increasing total costs by $55 million ($77 compared with $23 million). The number of maternal life-years gained per additional preterm birth varied from 68 to 258 with subsequent total costs per maternal life-years gained of $7,212 and $2,329.CONCLUSION: Cervical cancer screening every 3 years and subsequent treatment in women aged younger than 30 years yield limited life-years but may have substantial perinatal adverse effects. Consequently, women who plan to have children may benefit from a more cautious screening approach, taking into account their risk for both cancer and preterm birth.

Ivermectin is an effective drug for the treatment of human onchocerciasis, a disease caused by the parasitic filarial nematode Onchocerca volvulus. When humans are treated, the microfilariae normally found in the skin are rapidly and very nearly completely eliminated. Nonetheless, after a delay, microfilariae gradually reappear in the skin. This study is concerned with the causes of this delay. Hypotheses are tested by comparing the results of model calculations with skin microfilaria counts collected from 114 patients during a trial of five annual treatments in the focus area of Asubende, Ghana. The results obtained strongly suggest that annual treatment with ivermectin causes an irreversible decline in microfilariae production of approximately 30%/treatment. This result has important implications for public health strategies designed to eliminate onchocerciasis as a significant health hazard.

OBJECTIVE: To determine to what extent the higher impact of treatment for sexually transmitted diseases (STD) on HIV incidence in Mwanza, Tanzania than in Rakai and Masaka, Uganda might be explained by baseline differences between the trial populations. DESIGN: A re-analysis of baseline data from the three trial populations comparing demography, sexual risk behaviour and HIV/STD epidemiology. METHODS: Data were compared after age-standardization and adjustments for sample selection where necessary. STD rates were also adjusted for the sensitivities and specificities of the diagnostic techniques used. RESULTS: Demographic patterns were similar across populations, apart from effects of AIDS on fertility and mortality (including widowhood) in Uganda. Higher sexual risk behaviours, including younger age of sexual debut, higher numbers of recent partners and lower frequency of condom use, were apparent in Mwanza compared to Masaka and Rakai. High-titre serological syphilis, gonorrhoea, chlamydia infection and trichomoniasis were all more prevalent in Mwanza, except for chlamydia infection in males. There was little difference between sites in the seroprevalence of Herpes simplex virus type-2. Age patterns in the prevalence of short-duration STD and current risk behaviours were similar across sites but all-titre serological syphilis was more prevalent among older participants in Rakai and Masaka than Mwanza. CONCLUSIONS: Differences between trial populations included higher reported risk behaviour and higher rates of curable STD in Mwanza compared to Rakai and Masaka. These differences probably relate to previous reductions in risk behaviour in Uganda and may explain, at least in part, the contrasting results of these trials.