The purpose of this review is to integrate understanding of epidemiology and infertility. A primer on epidemiologic science and an example disease for which the design of epidemiologic investigations is readily apparent are provided. Key features of infertility that limit epidemiologic investigation are described and a survey of available data on the epidemiology of infertility provided. Finally, the work that must be completed to move this area of research forward is proposed, and, with this new perspective of “infertility as a disease,” improvements envisioned in public health that may be gained through improved understanding of the epidemiology of male infertility.
Key points
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The goal of epidemiologic research is to describe and interpret patterns of disease occurrence in populations in order to generate knowledge that can be used to prevent and/or treat disease.
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The epidemiology of male infertility is difficult to study for well-described reasons:
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Male infertility is not a reportable disease.
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Male infertility is diagnosed and treated in the outpatient clinical setting.
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Infertility care is often paid for out of pocket and, therefore, may not be noted on insurance billing.
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Frequently, the empiric treatment of male factor infertility involves assisted reproductive technology (in vitro fertilization) that primarily treats the female partner.
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The true nature of male infertility incidence remains elusive and the prevalence has been weakly estimated in heterogeneous studies.
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Equally perplexing is the assertion of a global decline in male infertility, with many contradictory studies leading to significant debate.
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One consistency throughout this review of literature is that male infertility is variable, with a multitude of influencing factors (race, country, geography, and unique at-risk groups), many of which need further study to better characterize them.
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Future, large-scale, prospective epidemiologic studies may help physicians bridge these gaps in understanding male infertility.
Introduction
Understanding the occurrence of disease in a population is important because it allows both quantifying and qualifying the burden of disease. Gaining such an understanding allows for societal preparedness, provides direction to scientists, and allows health care providers to counsel patients appropriately. Recently, infertility has been designated as a disease according to the Americans with Disabilities Act. This represents a difference from prior thinking, wherein infertility was deemed a disorder of inconvenience and its treatment considered elective. In contrast, a disease is defined as any deviation from or interruption of the normal structure or function of any part, organ, system, or combination thereof of the body that is manifested by a characteristic set of symptoms or signs. Based on this definition, male infertility meets these criteria.
The purpose of this review is to integrate understanding of epidemiology and infertility. A primer on epidemiologic science is provided and an example disease presented for which the design of epidemiologic investigations is readily apparent. Key features are then described of infertility that limit epidemiologic investigation and a survey of available data on the epidemiology of infertility provided. Finally, the work that must be completed to move this area of research forward is proposed and what the epidemiology of infertility may be able to teach 20 years from now described. Lastly, with this new perspective of “infertility as a disease,” improvements in public health that may be gained through improved understanding of the epidemiology of male infertility are envisioned.
Introduction
Understanding the occurrence of disease in a population is important because it allows both quantifying and qualifying the burden of disease. Gaining such an understanding allows for societal preparedness, provides direction to scientists, and allows health care providers to counsel patients appropriately. Recently, infertility has been designated as a disease according to the Americans with Disabilities Act. This represents a difference from prior thinking, wherein infertility was deemed a disorder of inconvenience and its treatment considered elective. In contrast, a disease is defined as any deviation from or interruption of the normal structure or function of any part, organ, system, or combination thereof of the body that is manifested by a characteristic set of symptoms or signs. Based on this definition, male infertility meets these criteria.
The purpose of this review is to integrate understanding of epidemiology and infertility. A primer on epidemiologic science is provided and an example disease presented for which the design of epidemiologic investigations is readily apparent. Key features are then described of infertility that limit epidemiologic investigation and a survey of available data on the epidemiology of infertility provided. Finally, the work that must be completed to move this area of research forward is proposed and what the epidemiology of infertility may be able to teach 20 years from now described. Lastly, with this new perspective of “infertility as a disease,” improvements in public health that may be gained through improved understanding of the epidemiology of male infertility are envisioned.
Epidemiology
The goal of epidemiologic research is to describe and interpret patterns of disease occurrence in populations in order to generate knowledge that can be used to prevent and/or treat disease. A majority of epidemiologic studies are based on the concept of identifying all cases of a disease in a defined population at risk. These disease cases are then studied in relation to the base population, from which they arose, in an effort to better understand the condition, generally for therapeutic purposes.
To better understand the power of epidemiologic research, it is useful to imagine a fictitious, prototypic disease, Disease Z (DZ). Imagine that several decades ago a physician was at a community hospital when he identified a patient with a unique set of symptoms and signs that led to severe respiratory failure requiring hospitalization. The patient had a circular rash on his chest unlike any the doctor had ever seen. This initially seemed an isolated case of disease but, over the next 3 months, the same physician cared for several more patients with respiratory disease of identical quality, all with the circular rash. The doctor described this case series in the Miscellaneous Journal of Disease , where he noted the pathognomonic finding of a circular rash, and he gave it the name, DZ. As a result of his publication, cases of DZ began being reported across the country with subsequent publication of several descriptive analyses from different hospitals. Doctors began to suspect that DZ accounted for more than 20% of patients who were hospitalized for acute respiratory failure. Because of the frequency, severity, and life-threatening nature of DZ, the Centers for Disease Control and Prevention (CDC) instituted a requirement that each case of DZ be reported to state public health authorities. No case of DZ escaped recognition due to the need for hospitalization and the unambiguous findings that made the diagnosis. The cause of DZ remained unclear and various therapies were trialed, including antibiotics, antifungals, and antiviral therapies; however, no one treatment seemed superior to another and patients with DZ did uniformly poorly, often never regaining normal pulmonary function. Two years after the first patient was identified with DZ, a researcher in Boston identified 51 patients hospitalized with DZ in a single city over a 1-year period. He compared these individuals with a second group of 290 patients, hospitalized in the same locations with routine viral or bacterial (non-DZ) pneumonia. His research team systematically reviewed the hospital records and, when necessary, interviewed the patients, their families, and their doctors. They compared the patients by age, race, occupation, socioeconomic status, and place of residence. They also compared patients by their other medical illnesses, medications, and their lifestyle habits, including tobacco smoking, alcohol consumption, and dietary habits. In doing so, they investigated no fewer than 45 potential risk factors as part of the same basic research design: studying each factor required just gathering more information about each subject. Furthermore, the information needed on these cases of DZ and the controls without DZ generally concerned events that had already happened by the time of data collection; therefore, the study could be completed quickly. As a result of this study, 2 factors, X and Y, were found associated with DZ, and patients with DZ had 3 and 4 times the exposure to X and Y, respectively, compared with those without DZ. When the research performed an analysis that grouped individuals by their race, it seemed that the association between X and Y and DZ was far more pronounced in patients of Asian descent relative to other patients. These findings prompted another group of doctors to treat their patients with DZ with a drug (Drug A) that was known to counter the effects of X and Y, and early success was reported in several observational studies. These strong associations also prompted the National Institutes of Health to sponsor a DZ prevention and treatment trial. This randomized controlled trial was specifically oversampled for Asian Americans and assigned one group to Drug A and the other to placebo. Among those individuals treated with Drug A, no cases of DZ developed compared with those not treated, in whom 10% developed DZ.
The story of DZ could go on further; however, it is clear from this narrative how epidemiologic research has the power to alter the future of DZ:
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It can identify the occurrence of disease in a base population.
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It can acknowledge an increase incidence in disease over time.
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It can identify risk factors for the disease that can narrow the search for a cause.
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And, it can identify subgroups of individuals who have elevated risk for disease, eventually designing interventional trials of prevention or treatment.
As with DZ, the full evaluation of any disease or condition that affects the human condition requires epidemiologic study if understanding and advancements in treatment are to be made. The study of male infertility is no exception.
In 2007, the Urologic Diseases of America (UDA) project consolidated the available literature and data on male infertility in an attempt to better understand the burden of disease. Unfortunately, the authors thought that insufficient data and literature were available to draw meaningful conclusions about the cause of infertility and the characteristics of infertile men.
To better define the disease demographics of male infertility, the authors think the epidemiologic characteristics of interest should include disease incidence, secular or birth cohort trends in diagnosis, racial variation, geographic variation, and infertility in unique exposure populations.
Infertility
Infertility is the inability to conceive after 12 months of regular, unprotected intercourse and infertility affects approximately 15% to 20% of all couples. The study of male infertility specifically presents a vexing clinical problem because both male and female partners make an independent contribution to a couple’s fertility; however, the outcomes of fertility are only manifested by conception or giving birth to a child. As a result, epidemiologic studies of male infertility present a formidable challenge, because the primary outcome of interest may be difficult to link to the male partner. This difficulty in confirming which partner makes the greatest contribution to a couple’s disease is a distinguishing characteristic of infertility and should be contrasted with DZ, in which pathognomonic findings make for diagnostic certainty.
Historically, epidemiologic and outcomes research has benefited from large repositories of administrative and reportable data. Such repositories take the form of insurance or Medicare claims, hospitalization records, or requirements from the federal government to report specific diseases and outcomes. There are limits to these types of data, however; such data derive strength in numbers and have the ability to represent large segments of the US population. Furthermore, these data sets are able to derive prevalence and, on occasion, incidence of a disease in a population at risk and help quantify disease burden. Understanding of these disease processes can then stimulate further research and, potentially, therapeutic innovations focused on the affected population.
There are several factors that have impeded the study of male infertility:
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Male infertility is not a reportable disease.
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For example, a diagnosis of prostate cancer is easily found within large-scale databases such as the Surveillance, Epidemiology and End Results database. Epidemiologic data are easily queried to quantify many aspects of disease, including prevalence, treatment outcomes, and survival characteristics, with the goal of improved therapies over time.
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Male infertility is diagnosed and treated in the outpatient clinical setting.
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Outpatient data are not accrued into large databases and, therefore, quantifying disease burden effectively is often not possible.
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Infertility care is often paid for out of pocket and, therefore, may not be noted on insurance billing.
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For obvious reasons, if there are no insurance claims to track diagnoses and treatments of a disease, it is difficult to quantify its true nature.
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Frequently, the empiric treatment of male factor infertility involves assisted reproductive technology (in vitro fertilization) that primarily treats the female partner.
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The CDC tracks in vitro fertilization outcomes and requires that an actual cause be assigned for the woman whereas only a single variable, “Male Factor–Yes/No,” is assigned for men.
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Whereas data were able to be compiled on DZ for advancement in understanding, given these described limitations with male infertility, there are neither repositories of data nor readily available means of identifying a population-based sample of infertile men in government, hospital, or standard claims data. As a result, most studies of male infertility to date have been case series data typically drawn from tertiary care or referral centers, population-based surveys, or high-risk occupational cohorts. For these reasons, a clear picture of the epidemiology or the underlying causes of male infertility in a population representative sample has never emerged.
Male infertility
Several areas of investigation provide evidence for the public health burden of male infertility. Reports have suggested that male infertility has been increasing over the past several decades; however, the extent and causes of declining male reproductive health remain largely unknown. The assertion that male infertility is increasing on a global level is controversial and challenging to confirm. Beyond the increasing burden of disease, male infertility causes significant psychosocial and marital stress and is associated with a high cost of infertility care. Recent work has suggested that male infertility may be associated with reduced longevity and that male factor infertility is an increased risk factor for certain malignancies. Although the cause of male infertility is understood in some cases (eg, cryptorchidism, specific genetic causes, and medical disease), most cases are due to poor semen quality (oligozoospermia, asthenozoospermia, or teratozoospermia—alone or in combination) of unknown causes. Additionally, up to 12% of couples have no identifiable cause of infertility.
Incidence of male infertility
Incidence is defined as the number of new cases of a disease in a specific population at risk over a specific period of time. This is in contrast to prevalence, which is defined as the total number of cases of disease (both old and new) present in a specified population at a single point in time. These terms are occasionally used incorrectly in medical literature but have important distinctions ( Fig. 1 ). Incidence is a rate at which a new disease occurs and is described as “cases per X number of person-time.” Prevalence, alternatively, is generally easier to calculate given that it may be assessed at a single point in time and is presented as a proportion of the total (%).
In order to calculate a disease incidence or prevalence, the base population at risk for a disease must first be defined. In the DZ example, all individuals in the Boston area were at risk for the disease; therefore, they comprise the denominator of the equation whereas new cases of DZ are the numerator. Frequently it is easiest to define a population at risk by geography, because population census data may be used as the denominator and, to date, most studies aimed at describing the incidence or prevalence of male infertility have done so for specific geographic regions.
Several efforts have been made to quantify the burden of infertility in certain parts of the world ( Table 1 ). Thonneau and colleagues deployed a cross-sectional design and conducted a large-scale survey of 1686 couples who were at risk for infertility in a specific French region in 1991. They were able to quantify prevalence despite the title mentioning incidence of infertility. Their principle findings were 14.1% overall infertility, with 39% having both a male and female component and approximately 20% due to male factors alone.
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