This study was conducted using the database of the Korea National Health and Nutrition Examination Survey to determine whether age-related eye diseases such as cataract, glaucoma, diabetic retinopathy (DR), and age-related macular degeneration (AMD), are related to hearing loss. 12,899 participants ≥ 40 years of age were included. The weighted prevalence of diabetic retinopathy was not significantly different between the normal hearing group and hearing-impaired group, but the weighted prevalences of cataract, glaucoma, early AMD, and late AMD were significantly different in the two groups. The odds ratio for cataract in the hearing-impaired group was 1.373 (1.118–1.687). The odds ratios of glaucoma, DR, early AMD, and late AMD were not significantly different in the hearing-impaired group. Age was significantly associated with the presence of concurrent cataract and hearing impairment by 6.574-fold per decade. Significant factors that increased the risk of concurrent glaucoma and hearing impairment were age, male gender, and triglyceride. Age, ex-smoker, systolic BP elevation, BMI decline, and fasting blood sugar significantly predicted the presence of concurrent DR and hearing loss. In early AMD, age and triglyceride, and in late AMD, age and systolic BP elevations increased the risk of concurrent AMD and hearing impairment. As people live longer, the incidences of hearing loss, vision loss, and the loss of both senses are increasing, and loss of both senses markedly diminishes quality of life. A previous study showed clear relations between all types of sensory loss and symptoms of depression and the likelihood of experiencing symptoms of depression is much higher, especially due to loss of two senses ,2. Dual The Korea National Health and Nutrition Examination Survey (KNHANES) is a population-based, cross-sectional epidemiological survey conducted by the Korean Ministry of Health and Welfare. In this survey, vision impairment is defined as a best corrected visual acuity (BCVA) of ≤0.32 in the better eye. KNHANES 2008–2009 showed the overall prevalence of visual impairment was 0.4% and that in individuals aged over 40 years it was 0.9% . In KoreAlthough various reports have documented eye diseases and hearing loss, no large-scale study has investigated their combined prevalence or the risk factors of specific eye diseases associated with hearing loss. In this study, we aimed to document the prevalences of eye diseases and of hearing impairment in individuals included in the KNHANES V, and to identify factors associated with concurrent eye disease and hearing impairment.KNHANES was conducted by the Korea Centers for Disease Control and Prevention to assess the health and nutritional status of the Korean population. Participants were selected by georegionally-based multistage probability sampling, stratified by sex and age. Using a weighting scheme, the survey produces estimated health statistics representing the noninstitutionalized Korean population. A more detailed description of KNHANES participants has been previously reported . As a paParticipants in KNHANES V over 19 years of age underwent full ocular examinations, including visual acuity, refraction, and intraocular pressure (IOP) measurements, and slit-lamp and fundus examinations with photographs. Refractive errors were evaluated by autorefraction . Bilateral IOP was measured once in the morning between 8:00 AM and 12:00 noon with Goldmann applanation tonometry and anterior segment was evaluated by slit-lamp  by ophthalmologists. The presence of pseudophakia or aphakia was determined. Anterior chamber angle width was estimated using Van Herick’s method. Eyes with a peripheral anterior chamber depth of less than a quarter of peripheral corneal thickness were classified as narrow angle eyes. Digital fundus images were acquired using a digital non-mydriatic retinal camera  and a Nikon D-80 digital camera . Images were acquired using physiologic mydriasis. Vertical and horizontal cup-to-disc ratios (VCDRs) were measured by glaucoma specialists using digital monoscopic fundus images.P < 1%, P < 0.05, or “not seen at maximum” on the total deviation plot. Frequency doubling technology perimetry  testing using the N-30-1 screening program was performed when a participant had an elevated IOP ≥ 22 mmHg or a glaucomatous optic disc with typical loss of the neuroretinal rim by slit-lamp biomicroscopy or fundus photography . FDT testing was repeated once if initial testing was deemed unreliable. The presence of an FDT examination abnormality was determined by comparison with the manufacturer’s internal normative database. Test results for visual field (VF) locations were classified into probability levels based on age-corrected normative values and depicted on a colored gray scale. An abnormal examination was defined by the presence of at least one test point of reduced sensitivity with Cataract was defined as the presence of nuclear, cortical, or posterior subcapsular opacity at least in one eye with a best-corrected visual acuity of <0.8 . GlaucomEar examinations were performed using a 4-mm 0-angled rigid endoscope  and the ML 150 vision system  by trained otolaryngologists and otolaryngology residents. Examination data were periodically evaluated by a quality control committee. Air-condition pure-tone thresholds were measured in a double-walled soundproof booth  using an automatic audiometer  for each ear at six frequencies  by well-trained examiners. Hearing loss was defined as pure-tone average thresholds at 500, 1000, 2000, 3000, 4000, and 6000 Hz averaged for both ears of > 40-dB. Hearing impairment was categorized by frequency (low or high) and severity (mild or moderate-to-profound). Low-frequency pure-tone was defined as the average pure-tone threshold at 500, 1000, and 2000 Hz. High-frequency pure-tone was defined as the average pure-tone threshold at 3000, 4000, and 6000 Hz, and mild hearing impairment was defined as an unaided pure-tone mean of 26–40 dB. Moderate-to-profound hearing impairment was defined as an unaided pure-tone mean of >40 dB.A history of noise exposure was obtained using questionnaire responses. Occupational exposure to noise was as a history of loud noise at work for >3 months that required speaking with a loud voice. Environmental exposure to noise was defined as exposure to loud noise for >5 hour/week outside work requiring speaking with a loud voice.Those with a smoking history were categorized as non-smokers, ex-smokers, or current smokers. Alcohol consumption was determined by asking participants about drinking behavior during the previous year. Height, weight, and waist circumference were measured during a physical examination. Body mass index was calculated by dividing subject weight in kilograms by the square of subject height in meters. Systolic and diastolic blood pressures were measured in the sitting position after a minimum of 5 minutes of rest. Fasting serum glucose, triglyceride (TG), total cholesterol, and high-density lipoprotein cholesterol (HDL-C) levels were measured using conventional methods. P-values of < 0.05 were considered significant. The data analysis was performed using weighted data; SEs of mean population estimates were calculated using Taylor linearization methods. Participant characteristics were expressed as means and SEs for continuous variables and as frequencies and percentages for categorical variables.The statistical analysis was performed using IBM SPSS Statistics for Windows/Macintosh, Version 23.0 . Group baseline demographic information and clinical parameters were compared using Pearson’s chi-square test for categorical variables and the general linear model for continuous variables. Univariate and multivariate logistic regression analyses were used to assess the risk ratios of specific ocular diseases according to the presence or absence of hearing impairment. Logistic regression analysis was fitted with increasing degrees of adjustment using a model adjusted for age and sex (Model 1) and a model adjusted for age, sex, alcohol, smoking, systolic BP, diastolic BP, waist circumference, BMI, fasting blood sugar, total cholesterol, HDL, and TG (Model 2). Risk factors for concurrent specific ocular disease and hearing loss were also analyzed using univariate and multivariate logistic regression analyses.Of the 25,534 patients, 17,957 participants were eligible for both full ocular examinations and ENT examinations. The analysis excluded 2111 people with a history of occupational exposure to noise and 360 people with a history of environmental exposure to noise. In addition, 74 people with external auditory canal stenosis, 428 with perforation of the tympanic membrane, 348 with cholesteatoma, and 113 with chronic otitis media were excluded. We also excluded 1220 participants who were unable to undergo eardrum examination and 1236 who had no audiometric test. Finally, 12,899 people were eligible for inclusion in the study.P < 0.05).P < 0.05).P values < 0.001). The weighted prevalences of diabetic retinopathy were similar in the two groups (P = 0.740), and the weighted prevalences of early AMD and late AMD were significantly higher in the hearing-impaired group  than in the normal hearing group .P < 0.001). However, in Model 1, which was adjusted for age and gender, hearing loss only increased the risk of cataract 1.373-fold , and in Model 2, which was adjusted for age, sex, alcohol, smoking, systolic BP, diastolic BP, waist circumference, BMI, fasting blood sugar, total cholesterol, HDL, and triglyceride, hearing loss increased the risk of cataract 1.408-fold .The effects of hearing impairment on the weighted ORs of specific ocular diseases are shown in P = 0.002), and high frequency hearing impairment increased the risk of cataract 1.348-fold . Mild hearing impairment increased cataract risk 1.420-fold , and moderate-profound hearing impairment increased cataract risk 1.714-fold  as compared with the normal hearing group (In Model 1, hearing impairment in the low frequency region increased the risk of cataract 1.377-fold (95% CI, 1.119–1.693, ng group .The risk factors associated with the simultaneous presentation of a specific eye disease and hearing impairment and their odds ratios are presented in Ocular diseases that are severe enough to cause visual impairment may severely reduce quality of life. Hearing loss also impair the quality of life of the elderly, and the problem is even greater if the dual senses are lost. Thus, the identification of common risk factors of these multiple disorders might lead to the developments of effective preventive interventions ,12. We iVarious opacities of cortical, nuclear, and subcapsular lesions were more common in the hearing-impaired group than in the normal group. In addition, covariate-corrected models showed hearing impairment was associated with an elevated risk of cataracts. Klein et al. reported risk factors for concurrent age-related cataract and hearing loss by analyzing data obtained during the large population-based Beaver Dam Eye Study . Any typIn the elderly, hearing loss usually involves high-frequency hearing loss, which means that our analysis of the relation between high and low frequency hearing impairment and cataracts is important. Degeneration of the cochlear lateral wall, and particularly of the stria vascularis, is considered a major cause of presbycusis, which is sometimes referred to as “metabolic presbycusis”. A syndrIn Models 1 and 2, mild hearing impairment increased the risks of cataracts by 1.420 and 1.328-fold and moderate-profound hearing impairment increased risks by 1.714 and 1.617-fold, respectively, which means the greater the severity of hearing impairment, the greater the risk of cataracts. Since the majority of cataracts are surgically removable, we suggest patients with severe hearing impairment be checked for the presence of cataract.Kremmer et al. reported that patients with normal-tension glaucoma showed a significantly higher prevalence of hearing loss . Others The risk factors associated with concurrent glaucoma and hearing loss in the present study were age, male sex, and TG level. Thus, when a patient in the early stages of glaucoma with hearing impairment has a high TG level, treatment for hyperlipidemia might helpfully reduce the progressions of both diseases. Further research into the pathophysiology of glaucoma and hearing loss comorbidity is required undertaken.In diabetes mellitus, generalized vascular pathology is believed to cause changes in the inner ear , and a rIn the present study, the prevalence of diabetic retinopathy was similar in the hearing-impaired and normal hearing groups, indicating hearing loss is not associated with an increased risk of diabetic retinopathy. Risk factors associated with combined hearing loss and DR were age, ex-smoking, high systolic blood pressure, low BMI, and high fasting blood sugar. Monitoring auditory function and ophthalmic examination might be beneficial for the management of diabetic patients, especially those with these risk factors.In a previous large study that uses data from the Beaver Dam Offspring Study, early AMD was found to be significantly associated with hearing impairment , which iThe present study has several limitations that warrant mention. First, it is inherently limited by its cross-sectional design that limits the ability to explore the associations between hearing loss and ocular diseases. Second, although patients with chronic otitis media, ruptured tympanic membrane, and cholesteatoma were excluded, conductive hearing loss and sensorineural hearing loss could not be completely differentiated, and thus, we cannot affirm that all patients with conductive hearing loss were excluded. Third, unmeasured or residual confounding factors may have caused unexpected analytical bias. Fourth, VF was examined by FDT rather than by Humphrey field analysis, which is the test of choice for VF testing. However, FDT is a fast, reliable, and large-scale screening method that can detect glaucomatous VF defects earlier than standard automated perimetry . Fifth, Hearing loss and ocular diseases often co-occur, but the multivariate models used in the present study showed hearing loss was only associated with an increased risk of cataracts. The factors affecting the coexistence of cataract, glaucoma, diabetic retinopathy, or AMD and hearing loss differed by ocular disease, except for age. Previous studies have shown individuals with visual and auditory problems are at greater risks of physical, mental, and functional morbidities. We recommend auditory screening be performed on patients with impaired vision, and that hearing-impaired patients be checked for vision problems. In addition, we suggest that the common risk factors presented in this study be considered when screening limits are determined.


This study aimed to explore the risk of dementia in a middle- and older-aged population with severe or profound hearing impairments. Data were collected for the Korean National Health Insurance Service-National Sample Cohort from 2002 to 2013. Participants aged 40 or older were selected. The 4,432 severely hearing-impaired participants were matched 1:4 with 17,728 controls, and the 958 profoundly hearing-impaired participants were matched 1:4 with 3,832 controls who had not reported any hearing impairment. Age, sex, income, region of residence, hypertension, diabetes mellitus, and dyslipidemia histories were matched between hearing-impaired and control groups. The crude (simple) and adjusted  hazard ratios (HRs) of hearing impairment on dementia were analyzed using Cox-proportional hazard models. The severe hearing impairment group showed an increased risk of dementia . The profound hearing impairment group also showed an increased risk of dementia . Both severe and profound hearing impairments were associated with elevated the risk of dementia in middle- and older-aged individuals. Because of the degenerative nature of the disease, the preclinical phase of dementia precedes the onset of dementia by more than 20 years2. Until now, the most effective approach to preventing dementia is controlling the modifiable risk factors for dementia. Among the various risk factors of dementia, regular physical activity and management of cardiovascular risk factors (obesity and smoking), and lifelong cognitive training were reported as modifiable risk factors3. In addition, hearing impairment has been proposed as a modifiable risk factor4.Dementia is a prevalent neurodegenerative disorder with a high economic and social burden. It has been reported that approximately 1% of individuals 60–70 years of age and 6–8% of individuals 85 years of age or older are diagnosed with dementia each year worldwide4. However, few studies have investigated the risk of dementia in hearing-impaired subjects in wider age populations. Most previous studies have been conducted in populations 65 to 70 years of age and older8. For instance, a prospective cohort study reported that the moderate to severe hearing impairment was related to an increased risk of dementia in 70 and 79 years of age population5. On the other hands, a prospective study included younger subjects (ages 36–90 years) and found that severe hearing impairment increased the risk for all-cause dementia to 4.94 times that of the normal hearing control group9. However, this study had a small number of dementia subjects, with 37 cases of Alzheimer disease, and only 4 years of follow-up.Several previous studies have suggested a relation between dementia and age-related hearing declineThe hypothesis of the present study was that hearing impairment increases the risk of dementia in both middle- and older-aged subjects. To extend the findings of previous studies, this study used a large representative population and matched and/or adjusted several variables, including comorbidities. Furthermore, to explore the different impact of the hearing impairment on the risk of dementia according to severe or profound hearing impairment, the hearing impairment groups were divided into severely and profoundly hearing-impaired groups.The general characteristics of age, sex, income, region of residence, hypertension, diabetes mellitus, and dyslipidemia were matched between the hearing-impaired and control groups Table . DementiSevere hearing impairment increased the risk of dementia  Table .Table 2CWe conducted the subgroup analyses according to age groups Table . In the The present study demonstrated that severe or profound hearing impairment elevated the risk of dementia in a ≥40 years old population. This study extended previous findings by including a middle-aged population instead of confining analyses to an older-aged population. As a result, middle-aged and older-aged adults with severe hearing impairments showed an increased risk of dementia. Moreover, profound hearing impairment showed higher HRs for dementia than for severe hearing impairment in this study.10. In a prior study, mice with moderate hearing impairment showed decreased learning and cognitive functions compared to normally hearing mice11. Although the present study investigated the new onset dementia after diagnosed as hearing impairment, the reverse causality might be possible between dementia and hearing impairment. Dementia or mild cognitive dysfunction was suggested to induce central auditory dysfunction13. Indirectly, the auditory deficit might make it difficult to communicate verbally, which may result in social isolation and contribute to cognitive impairment and subsequent dementia14. Emotional problems, including depression in hearing-impaired subjects, also contribute to the risk of dementia. The difficulties of hearing-impaired subjects in engaging in social activities have been suggested to induce psychological problems, including depression15. Loneliness and depressive symptoms could precipitate the risk of dementia16.Several plausible pathophysiologic mechanisms were suggested for the risk of dementia in hearing-impaired subjects. The deprivation of auditory sensory input might precipitate the decline of cognitive function in severely hearing-impaired subjects. A prospective cohort study of individuals aged 70–79 years demonstrated that hearing impairment was a risk factor for impairments of global cognitive and executive functions17. White matter tract degeneration has been reported in early-stage dementia of the Alzheimer type18. Subjects with peripheral hearing impairment could progressively develop central hearing impairment at a later phase, with severe to profound hearing impairment19. Moreover, a recent electrophysiological study demonstrated that the central speech processing neural activity from the brainstem was impaired before other behavioral deficits occurred in mild cognitive impairment20. The common etiologic factors, including vascular compromise, inflammation, metabolic disturbance, and other hormonal factors between hearing impairment and dementia, might indicate an unraveling of shared pathophysiologic mechanisms. Future studies should delineate these links between hearing impairment and dementia.In addition to the causal relationship between hearing impairment and dementia, it was suggested that hearing impairment and dementia might coincide due to common neurodegenerative processes. Hearing impairment could be a harbinger of declining cognitive function. In particular, subjects with central auditory processing disorders could have central nervous dysfunction in non-auditory areas. Studies have suggested that degenerative changes in the central nervous system, including smaller brain volumes and white matter tract dysfunctions, may occur in hearing-impaired subjects14. Because middle-aged adults have more opportunities to participate in social communication and often have jobs, they could be more affected by impaired verbal communication. In contrast, older adults tend to be more accommodating and often do not perceive their hearing impairment22. Thus, older adults might be more protected than middle-aged adults from the social isolation and emotional problems due to hearing impairment. In addition, subclinical cognitive impairments could initially manifest as a central auditory processing disorder in middle-aged adults as well as in older adults. Because early-onset Alzheimer disease in adults younger than 65 years of age has more aggressive features and atypical presentations compared to the more common late-onset Alzheimer disease, early detection and screening for severely hearing-impaired individuals might be crucial to relieving potential social and economic burdens23. Severe hearing impairment increased the risk of dementia in older adults but not in adults younger than 65 years of age in this study. The smaller number of severe hearing impairment and dementia patients in younger population than older adults might contribute to the less statistical power to demonstrate the risk of dementia in severe hearing impairment in adults younger than 65 years of age.This study demonstrated that the risk of dementia was higher in both middle-aged and older adult populations. The middle-aged adults demonstrated higher HR of dementia for profound hearing impairment than old adults in this study. The adverse impacts of profound hearing impairment could be stronger in middle-aged adults than in older adults, although the prevalence of hearing impairment is higher in older adults. It was reported that social isolation was higher in hearing-impaired adults aged 45–59 years than in hearing-impaired adults aged 60 years or older24. Despite a small sample size and a poorly adjusted or controlled study design, a previous study suggested that the degree of hearing impairment was related to the degree of cognitive deficit in both aided and unaided hearing-impaired subjects25. Some studies of participants with moderate or lower levels of hearing impairment have reported a weak association between hearing impairment and dementia or cognitive dysfunction. A longitudinal study did not show a relation between hearing impairment and mild cognitive impairment, which is probably because the study defined hearing impairment based on the whispered voice test rather than the pure tone audiometry (PTA)12. More deficits of auditory sensory deprivation might contribute to the elevated risk of dementia.In the present study, profound hearing impairment showed a higher HR for dementia compared to severe hearing impairment. An animal experiment demonstrated that the degree of hearing impairment was correlated with neural degeneration in the hippocampus26. A previous research group analyzed the HRs for comorbid conditions, including disease of the eye and adnexa, endocrine and circulatory disease, musculoskeletal disease, respiratory disease, and nervous and mental disorders, in dementia patients using the NHIS-NSC data26. The Korea Dementia Comorbidity Index was derived from these HRs for comorbidities which as superior to other comorbidity indices for dementia26. In addition, the prevalence of dementia in this study was comparable to that of central dementia center of Korea  approved the use of these data. Written informed consent was exempted by the Institutional Review Board. All methods were performed in accordance with the guidelines and regulations of the ethic committee of Hallym University.27. The details of the methods used to perform these procedures are provided by the National Health Insurance Sharing Service28. This cohort database included (i) personal information, (ii) health insurance claim codes (procedures and prescriptions), (iii) diagnostic codes using the International Classification of Diseases-10 (ICD-10), (iv) death records from the Korean National Statistical Office (using the Korean Standard Classification of Diseases), (v) socio-economic data (residence and income), and (vi) medical examination data for each participant from 2002 to 2013.This national cohort study relied on data from the Korean National Health Insurance Service-National Sample Cohort (NHIS-NSC). The Korean National Health Insurance Service (NHIS) selects samples directly from the entire population database to prevent non-sampling errors. Approximately 2% of the samples (one million) was selected from the entire Korean population (50 million). These selected data can be classified at 1,476 levels  using randomized stratified systematic sampling methods via proportional allocation to represent the entire population. After data selection, the appropriateness of the sample was verified by a previous studyBecause all Korean citizens are recognized by a 13-digit resident registration number from birth to death, exact population statistics can be determined using this database. It is mandatory for all Koreans to enroll in the NHIS. All Korean hospitals and clinics use the 13-digit resident registration number to register individual patients in the medical insurance system. Therefore, the risk of overlapping medical records is minimal, even if a patient moves from one location to another. Moreover, all medical treatments in Korea can be tracked, without exception, using the Health Insurance Review and Assessment (HIRA) system. In Korea, the notice of a death to an administrative entity is legally required before a funeral can be held.Out of 1,025,340 cases with 114,369,638 medical claim codes, we included participants who were registered as a hearing-impaired person in the Ministry of Health and Welfare. These individuals were divided into 2 groups based on the degree of impairment: severe hearing impairment  and profound hearing impairment (hearing threshold ≥90 dB in both ears). In Korea, to be registered as a hearing-impaired person, individuals must be checked 3 times using a pure tone audiometry test (PTA) and 1 time using an auditory brainstem response. The average hearing threshold of PTA was calculated as follows: (500 Hz + 2 * 1000 Hz + 2 * 2000 Hz + 4000 Hz)/6.A total of 5,135 severely hearing-impaired and 1,390 profoundly hearing-impaired participants were selected. Of these participants, those who were registered with other disabilities  in the Ministry of Health and Welfare were excluded . We also excluded participants under 40 years of age in 2002 . The hearing-impaired participants were matched 1:4 with participants (control group) who had never been diagnosed with a hearing impairment or other disability from 2002 through 2013. The matches were processed for age, group, sex, income group, region of residence, hypertension, diabetes mellitus, and dyslipidemia histories. However, they were not matched for ischemic heart disease, cerebral stroke, and depression because strict matching increases the number of excluded study participants due to a lack of control participants. After matching, we analyzed the participants’ histories of dementia in both the hearing impairment and control groups. To prevent selection bias when selecting the matched participants, the control group participants were sorted using a random number order, and they were then selected from top to bottom. It was assumed that the matched control participants were involved at the same time of each matched hearing-impaired participant. Therefore, members of the control group who had died before the time of involvement of the matched hearing-impaired participants were excluded. The hearing-impaired participants for whom we could not identify enough matching participants were excluded . The participants who had dementia before being diagnosed with a hearing impairment were excluded . Finally, a 1:4 matching resulted in the inclusion of 4,432 severely hearing-impaired participants and 17,728 corresponding control participants and 958 profoundly hearing-impaired participants and 3,832 corresponding control participants  or dementia in Alzheimer’s disease (F00). For the accuracy of diagnosis, we selected these options only if the participants were treated ≥2 times. We have described the reliability of the diagnosis of dementia in the supplement . A total of 10 age groups were designated. The income groups were initially divided into 41 classes . These groups were re-categorized into 11 classes . The health insurance population was grouped according to 10 deciles. This re-categorization was performed by NIHS to de-identification of participants. Regions of residence were divided into 16 areas according to administrative districts. These regions were regrouped into urban  and rural  areas.30. For the accuracy of diagnosis, hypertension (I10 and I15), diabetes (E10–E14), and hyperlipidemia (E78) were selected if participants were treated ≥2 times. Ischemic heart disease (I24 and I25) and cerebrovascular disease (I60–I66) were selected if participants were treated ≥1 time. Depression was defined using the ICD-10 codes F31 (bipolar affective disorder) through F39 (unspecified mood disorder), as specified by a psychiatrist from 2002 through 2013.The past medical histories of participants were evaluated using ICD-10 codes as previous studiesChi-square tests were used to compare the general characteristics between the hearing-impaired group and the control group.To analyze the HR of hearing impairment on dementia, Cox-proportional hazard models were used. In this analysis, crude (simple) and adjusted  models were used. In addition, 95% CIs were calculated. To analyze the cumulative probability of dementia in the hearing-impaired and control groups, Kaplan-Meier survival analysis was conducted and Kaplan-Meier plot was provided. Cox proportional hazards model test was performed. For the subgroup analysis, we divided the participants by age (<65 years old and ≥65 years old).Two-tailed analyses were conducted, and P-values less than 0.05 were considered significant. The results were statistically analyzed using SPSS v. 21.0 .Supplementary Table S1


Bolajoko Olusanya talks to Sophie Cousins about her personal experience with hearing impairment and her efforts to advance paediatric hearing health in Nigeria. SC: I understand your work has been inspired by your personal experience of congenital hearing disability. Is that correct?BO: That’s right, I was born with mid-frequency hearing loss and struggled with inter-personal communication as a child without knowing what was wrong. In fact, I wasn’t diagnosed until I was 33 years old.SC: How did that come about?BO: I was doing a hearing test for a child with otitis media at a community centre for child development in East London and was surprised to find the child responding to sound levels that I couldn’t hear myself. After discharging the child, I immediately tested my own hearing. The audiogram showed a ‘cookie-bite’ mid-frequency bilateral hearing loss, which was subsequently confirmed by the centre audiologist.SC: That must have been quite a revelation.BO: It was. It was also something of a turning point for me. It brought back a lot of memories of my childhood including being ridiculed and taunted. I grew up an angry child from frequent and unjust punishment for not doing what I was told even though I always did what I heard. It really made me think, particularly about all the children with hearing loss who were going through similar struggles, but without the advantages that I had. I felt morally bound to do something for those children and decided to use all my education and training to draw attention to the challenges they and their families face. I started by conducting an audiological survey of children in Lagos schools in 1994.SC: What did you find?BO: Remarkably, I found that 13.9% of school entrants had some degree of hearing loss. Frankly it surprised me, but it was later corroborated by a more robust WHO-supported national hearing survey, which was carried out between 1999 and 2001 and reported a national prevalence of 13.4% among school children. My research showed not only the true prevalence of hearing loss among children in Nigeria, but the range of personal coping strategies they were using to be able to function and to avoid discrimination and stigma.SC: Are discrimination and stigma significant issues in Nigeria?BO: Yes, hearing impairment is associated with superstitions in many communities, which feed into both. However, the major reason for the issue’s neglect and, so to speak, ‘invisibility’ is the fact that it is not associated with significant mortality.“[I] wanted to galvanise research that could translate into policy initiatives.”SC: Can you explain what you mean by that?BO: Nigeria, like most developing countries, is greatly influenced by global health priorities and funding programmes, which tend to prioritise reducing mortality or ensuring survival rather than supporting or promoting well-being.SC: Is that why you set up your different non-governmental organisations – to address that neglect?BO: Certainly, that was part of it, but I also wanted to galvanise research that could translate into policy initiatives for the public good. Basically, after I completed my studies I was faced with a choice: I could pursue a professorial career in my university, which would of course mean working within the administrative constraints of academia or become a social entrepreneur with unconventional community engagement on issues that I was passionate about. I chose the latter and set up Hearing International Nigeria (HING), which was officially launched in 1999 by Professor Peter Alberti, the then Secretary-General of the International Federation of Otorhinolaryngological Societies. I also received a great deal of support from Professor Robert Ruben, the Editor-in-Chief of the International Journal of Pediatric Otorhinolaryngology.SC: What was the main focus of your work with HING?BO: I worked on the development of effective child health policy based on the research I had done on hearing loss prevalence. I conducted a new study of two schools for deaf children with the aim of determining modifiable causes or risk factors for hearing impairment. I found that a high proportion of the children had congenital hearing loss, and the parents only provided very limited insights on the underlying causes, so it was something of a dead end. To gain more insight into audiological medicine I committed to pursuing a PhD in Child Health at UCL with Professor Linda Luxon. I formed the Nigerian Dyslexia Association which was combined with HING into the Centre for Healthy Start Initiative, the umbrella nongovernmental organization for all my research and advocacy activities, in 2011.SC: What inspired you to start an organisation focused on dyslexia?BO: I could see that there was a lack of awareness about dyslexia in Nigeria and a lack of services in support of people living with the condition. However, as with the hearing impairment issue, my personal experience was a key motivator. My husband is dyslexic, as is one of my children. The NDA creates awareness and training for teachers on dyslexia in mainstream schools and advocates for support for children with learning disabilities. Inspired by my husband’s success story as an accomplished banker, we have been able to advocate and to support awareness programmes and training for teachers. I am pleased to report that a number of children who have been helped by the programme have gone on to university, including my son who has a master’s degree in software engineering from the UK and is a successful entrepreneur in Nigeria.SC: You also worked on developing hospital and community-based infant hearing screening programmes in Nigeria. Can you talk about that?BO: To get a clearer picture of the epidemiology of hearing loss, to understand the drivers of incidence and potential public approaches to prevention, treatment and support, you need to implement effective screening programmes. Because of the high proportion of babies not born in hospitals, I considered it essential to have a community-based universal infant hearing screening programme, to compliment the hospital-based programme. The programme was launched in 2005. About 12 000 mother-infant pairs were enrolled – 5 000 for the hospital-based programme and 7 000 for the community-based programme. The programmes constituted a very effective platform for investigating the profile of hearing loss in newborns and any related maternal factors in great detail and were the basis for numerous novel findings.“Opportunities are emerging […] for collaboration across disciplines.”SC: Can you be specific?BO: One key finding was the contribution of severe neonatal jaundice to the incidence of hearing loss. This finding led to several interventions to reduce the incidence of severe jaundice in the two centres where the newborn hearing screening programmes were conducted. For instance, we were able to install a simple low-cost technology that uses filtered sunlight for treating children with jaundice. From its inception in 2011, the centre has been providing free screening and treatment for jaundice for all newborns at two state hospitals as part of its community engagement to reduce the incidence of bilirubin-induced disabilities. We were also able to show how routine immunisation clinics could deliver essential child health services such as newborn hearing screening and baseline anthropometry for growth monitoring to infants not born in hospitals. Finally, we were able to make a number of observations regarding maternal health, mode of delivery, health-seeking behaviour, immunisation uptake and undernutrition. For example, because we enrolled participants under the hospital-based programme during maternal visits to antenatal clinics, we were able to track the incidence of stillbirths and related maternal risk factors. Unfortunately, both programmes were terminated in 2008 due to funding constraints and the lack of government-backed public health policy at the state and federal levels.SC: You have talked about the importance of cross-disciplinary collaboration for the prevention and management of early childhood disabilities. To what extent is this approach being used in Sub-Saharan Africa?BO: Children living with disabilities need the support of a wide range of specialists, including paediatricians, psychologists, neurologists, audiologist and otorhinolaryngologists – to name just a few. Establishing and coordinating the services from these various disciplines efficiently for the benefit of the patient is often a challenge. The need for coordination and collaboration is even greater in sub-Saharan Africa where there is an acute shortage of skilled personnel. Working with children and their families, I believe that paediatricians or other childcare specialists are in a better position to coordinate and lead such collaboration. Unfortunately, the implementation of this approach has so far been hindered by the lack of policy initiatives at the national or global level to address early childhood disabilities. It is only now that opportunities are emerging through the sustainable development goals for collaboration across disciplines.


Hearing loss prevalence data in South Africa is scarce, especially within primary health care settings.To determine; (i) the prevalence of hearing disorders in patients ≥3 years of age attending two primary health care clinics, and (ii) the nature and characteristics of hearing disorders at these primary health care clinics.A cross-sectional design was used at two primary health care clinics. Non-probability purposive sampling was used to screen participants at clinics for hearing loss with pure tone audiometry. A total of 1236 participants were screened (mean age 37.8 ±17.9 years). Diagnostic testing was available for confirmation of hearing loss on participants who failed the screening.Hearing loss prevalence was 17.5% across both clinics. Most hearing losses were bilateral (70.0%) and were of a sensorineural nature (84.2%).Hearing loss prevalence was comparable at both primary health care clinics. Participants 40 years and older were at significantly higher risk for hearing loss. The current study is the first attempt to establish hearing loss prevalence for primary health care clinics in South Africa. Hearing loss is a major public health concern affecting more than 1.33 billion people globally in 2015Hearing loss has a devastating effect on the individual with some of the resulting sequelae including academic failure, higher unemployment, poorer general health, social isolation and an increased incidence of depressionApart from a preliminary population-based study conducted in the Cape Town metropolitan areaThis research project was approved by the Institutional Research Board of the University of Pretoria, South Africa and was part of a larger community oriented primary care (COPC) project in Gauteng province in the City of TshwaneA cross-sectional design was used at two PHC clinics (PHC clinic 1 and PHC clinic 2) situated in different underserved communities in Tshwane. Non-probability purposive sampling was used to screen participants as it was a clinical, non-experimental set-up and results would therefore be representative of the clinic population. At PHC clinic 1, universal screening took place by offering all individuals who visited the clinic a hearing screening free of charge. At PHC clinic 2, all individuals who were available during the time that the services were delivered and who wanted their hearing tested were screened free of charge. Only individuals visiting the clinic as patients were selected as participants. Diagnostic testing was available for confirmation of hearing loss on participants who failed the screening. Participants aged three years and older were included in the study. This criterion was included as the preferred method of testing children younger than 3 years of age is visual response audiometry (VRA) which was not available at the clinics. Only participants who provided signed consent  and who completed the screening protocol  were included in the study. Instructions were provided in English or Afrikaans. Written instructions in Sepedi were used if participants did not understand English or Afrikaans. If participants were unable to understand one of these three languages, a health care nurse who was available at the specific time, was asked to translate the information. Participants who presented with a mixed or conductive hearing loss were referred to the clinics' general practitioner for further medical examination and intervention. Participants who presented with a sensorineural hearing loss (SNHL) were referred to the nearest district hospital for a hearing aid fitting evaluation.Hearing screening was facilitated by undergraduate audiology students from the University of Pretoria under supervision of an experienced audiologist (first author). Otoscopy was performed with a handheld Welch Allyn otoscope  as a pre-screen to determine any obvious abnormalities of the external ear canal or tympanic membrane. Any obvious abnormalities of the external ear canal or tympanic membrane were noted. Testing was conducted in an examination room without sound isolation. Due to time and facility constraints at clinics more than one participant was examined at the same time in a room in some instances. In these instances, more than one student audiologist was available to evaluate participants. Smartphone hearing screening was performed using two sets of Samsung Galaxy Pocket Plus S5301 phones running the validated hearScreen™ Android OS application with calibrated supra-aural Sennheiser HD202 II headphones To determine the prevalence and nature of hearing loss, pure tone audiometry was performed on the same day on participants who failed the screening for a second consecutive time. Automated pure tone audiometry (air- and bone conduction) was performed at 0.5, 1, 2, 4 kHz using a Type 2 Clinical Audiometer . Insert earphones were placed deep in the ear canal with circumaural headphones placed over the ears to improve attenuation of ambient noise, and to minimize the occlusion effect. An automated threshold-seeking paradigm was utilized with a similar threshold-seeking method used in manual test configuration i.e. the modified Hughson-Westlake method. Air and bone conduction thresholds were determined with masking of the non-test ear when indicated. The software actively monitored ambient noise levels across octave bands throughout the test procedures in both clinics. Whenever the noise exceeded the maximum ambient noise level allowed for establishing a threshold, the test operator waited for the transient noise to subside.Diagnostic testing confirming a hearing loss informed the prevalence rate for this population. The presence of a hearing loss was defined as a pure tone threshold average  greater than 25 dB HL in one or both earsA total of 1236 participants were included in the study  . The meaOne hundred and twenty (9.7%) of the participants (mean age 49.8±19.8 years) presented with a confirmed hearing loss  . When thThirteen participants (10.8%) were diagnosed with combinations of SNHL, conductive or mixed losses in respective ears . NineteeHearing loss prevalence data in Africa varies greatly. The current study revealed a hearing loss prevalence of 17.5% at two PHC clinics in underserved communities in the Tshwane area. This is slightly higher than the 12.35% prevalence reported in the Cape Town metropolitan areaThere is an upward trend in hearing loss prevalence with increasing ageIn the same age group in Europe at least 30% of men and 20% of women are affected by a hearing lossA recent systematic review demonstrated that middle ear disease, that could ultimately lead to conductive hearing loss, is the most common cause not only in the schoolaged population, but also in the general populationWith the great variation in hearing loss prevalence due to different contexts, screening techniques and different age groups, it is imperative to recognize the lack of population-based studies in Africa. Hearing loss is a significant public concern being a highly prevalent and chronic condition. Individuals and communities are challenged by a variety of adverse effects as well as high costs. The negative effects and expenses in communities could be alleviated by implementing hearing care at PHC level. Implementing ear and hearing services at PHC level will require careful planning and identification of specific program goals and specification of care pathways. The current study is the first attempt in establishing hearing loss prevalence baselines and descriptions at PHC clinics. Novel hearing detection solutions, capitalizing on advances in technology and connectivity, which were used in the current study, provide the possibility to expand and decentralize hearing care services to PHC level.A limitation of the study was that participants younger than three years of age were not included. Furthermore, the population was sampled purposively and not randomly taking into consideration power and precision. This was mainly due to the clinical time and human resource constraints of the research setting.


However, there is no systematic review and meta-analysis study. Thus, we aimed to meta-analyze the existing evidence on the association of iron deficiency anemia (IDA) with sensorineural hearing loss (SNHL). We searched PubMed, MEDLINE, Embase, Scopus, and Google Scholar from inception through October 30, 2017, for studies done on the association of the IDA with SNHL. Pooled odds ratio (OR) was calculated by random effect meta-analysis method. Heterogeneity was assessed by IFour studies, covering a total of 344,080 adults and children, were included. The odds of SNHL was higher by 55% in individuals with IDA, compared with individuals without IDA . The age-specific ORs were 1.36  and 3.67 (95% CI 1.72–7.84) for adults and children, respectively. IDA may be a contributing factor to hearing loss. Further studies are warranted, including whether IDA treatment reduces the risk of hearing loss. Meanwhile, hearing loss screening in anemic individuals, or vice versa, may represent an important consideration.PROSPERO registration CRD42017082108The online version of this article (10.1186/s13104-019-4323-z) contains supplementary material, which is available to authorized users. Anemia remains among the major global public health problems, affecting almost a third of the world population (over 2.2 billion people) , 2. ThouExisting evidence showed IDA linked to hearing loss. A higher prevalence of hearing loss was reported in individuals with IDA , 16. TheThis systematic review and meta-analysis was conducted following the recommendations of Meta-analysis of Observational Studies in Epidemiology guideline , and theWe searched PubMed, MEDLINE, Embase, Scopus, and Google Scholar for studies published in English from inception to June 30, 2018. We used a combination of search terms including ‘anemia’, ‘anaemia’, ‘iron deficiency anaemia’, ‘iron deficiency anemia’, ‘iron-deficiency anaemia’, ‘iron deficiency anemia’, ‘hearing loss’, ‘deafness’, ‘hearing impairment’, and ‘hearing dysfunction’.The inclusion criteria were (1) hearing loss measured and reported, (2) IDA measured and reported, and (3) adjusted OR, incidence rate (IR), or relative risk (RR) reported on the association of anemia and hearing loss. The exclusion criteria were any one of the following conditions: (1) animal studies; (2) celiac disease; (3) anemia of causes specified other than iron deficiency; or (4) qualitative studies, commentaries, editorials, and case reports.Literature search, title, abstract and full-text screening and reviewing were done by SHM and SA, working independently and in duplicate. The screening and selection processes are illustrated using the PRISMA flow diagram  . The gra2-statistics, which quantifies the proportion of variance explained by between-study heterogeneity. According to Higgins et al. [2 < 25%, 25–49%, 50–75%, and > 75% represents no, low, moderate and high levels of heterogeneity, respectively. Potential sources of heterogeneity were identified using subgroup analyses. We intended to assess publication bias using the funnel plot technique, Begg’s rank test, and Egger’s regression test, as appropriate. However, it was not possible as there were inadequate numbers of studies, which under-power any of these methods. A minimum of 10 studies is recommended to evaluate publication bias, although a meta-analysis of 2 or more studies is possible [The OR was used to summarise the estimates, representing the odds of hearing loss in those with IDA, compared with those without IDA. The summary OR was calculated with the DerSimonian and Laird random effect model, which accounts for both within study and between studies variations . Heterogs et al. , I2 < 25We included a total of four studies –18 in th2 = 66.9%, P = 0.03 for heterogeneity). Subgroup analysis was conducted by age groups, separating studies on adult and children. Figure 2 = 23.9%, P = 0.27), but the associations remained significant and almost the same . Thus, the summary estimate of odds of SNHL associated with IDA was 1.36  in adults and 3.67 (95% CI 1.72–7.84) in children and adolescents.Figure We found a significant association between IDA and SNHL among both adults and children. The odds of SNHL was 55% higher in individuals with IDA, compared with those without IDA. A recent study in the USA showed that the prevalence of hearing impairment was 3.0% in children with IDA, but 1.7% in those without IDA . In geneThe mechanism linking IDA and SNHL is uncertain and non-conclusive, through some plausible mechanisms were proposed. A more frequently mentioned mechanism is the vascular hypothesis that the cochlea is susceptible to ischemia due to lack of collateral circulation. Provided iron is essential for the production of hemoglobin, one could presume IDA will likely compromise tissue oxygen delivery , 4, 18 aAnemia and hearing loss are among the top leading contributors to health impairment globally . Anemia While IDA remains a major public health problem globally, its association with hearing loss would be more concerning. The existing evidence is limited on whether anemia prevention and treatment reduces the risk of hearing loss or treatment of anemia in individuals with concurrent anemia and hearing impairment improves hearing function. Although the design of the studies included in our work precluded making a causal inference and definitive conclusion, we suggest further studies on the feasibility and effectiveness of hearing loss screening for individuals with anemia or anemia screening for individuals with hearing loss. Prevention, early diagnosis, and treatment of IDA could have dual benefits, reducing the burden of anemia and probably hearing loss. Previous reports also recommended the importance of screening , 17, 18.This study showed a significant association between IDA and hearing loss. The exact mechanism by which IDA contributes to hearing loss, and whether IDA treatment reduces the risk of hearing loss is unclear. Further studies are warranted to better understand the link between IDA and hearing loss and draw public health attention. Besides, the potential of integrating hearing loss screening for anemic patients, or vice versa, needs to be further explored.We found only four studies, which underpowered any of the existing publication bias evaluation methods.Except one study done from Taiwan, the other studies refer only to the US population, limiting the generalizability of the findings to the rest of the world.All studies were observational in design, precluding making causal inferences.Additional file 1. PRISMA flow diagram of study selection and screening.


Drosophila melanogaster is characterized by repeated execution of distinct, stereotyped actions in variable order. Experiments demonstrate that, following stimulation by an irritant, grooming progresses gradually from an early phase dominated by anterior cleaning to a later phase with increased walking and posterior cleaning. We also observe that, at an intermediate temporal scale, there is a strong relationship between the amount of time spent performing body-directed grooming actions and leg-directed actions. We then develop a series of data-driven Markov models that isolate and identify the behavioral features governing transitions between individual grooming bouts. We identify action order as the primary driver of probabilistic, but non-random, syntax structure, as has previously been identified. Subsequent models incorporate grooming bout duration, which also contributes significantly to sequence structure. Our results show that, surprisingly, the syntactic rules underlying probabilistic grooming transitions possess action duration-dependent structure, suggesting that sensory input-independent mechanisms guide grooming behavior at short time scales. Finally, the inclusion of a simple rule that modifies grooming transition probabilities over time yields a generative model that recapitulates the key features of observed grooming sequences at several time scales. These discoveries suggest that sensory input guides action selection by modulating internally generated dynamics. Additionally, the discovery of these principles governing grooming in D. melanogaster demonstrates the utility of incorporating temporal information when characterizing the syntax of behavioral sequences.Mathematical modeling of behavioral sequences yields insight into the rules and mechanisms underlying sequence generation. Grooming in  Drosophila melanogaster grooming behavior consists of many complex sequences involving repetitions of well-characterized actions. In this paper, we leverage advances in machine vision to automatically annotate over 40 hours of video data of flies covered in dust and develop mathematical models that reveal the existence of syntax in D. melanogaster grooming. We find that sequence organization depends on grooming action identity, as has been well-established, and, more surprisingly, grooming action duration. The discovery of duration-dependent action selection leads us to conclude that, although sensory input informs grooming decisions on long time scales, internal dynamics also guide individual transitions between grooming actions. Therefore, incorporating action duration into our models allows us to uncover multi-scale temporal dynamics that suggest the existence of neural circuits dedicated to partially sensory-independent decision-making. Our approach highlights the importance of incorporating temporal information into sequential models, as doing so reveals the relative contributions of sensory input and internal dynamics to behavioral sequence generation.Analysis of temporally rich behavioral sequences provides a quantitative description of the rules underlying their generation.  Sequential animal behaviors are often composed of repetitions of simple subroutines. In driving motor action execution, nervous systems must integrate sensory information with internal priorities and dynamics. Both external and internal conditions contribute to sequence organization, but their respective weights are unknown. At one extreme, purely sensory-driven, reflexive processes produce actions, such as larval escape sequences, solely based on recent sensory input . At the Drosophila melanogaster, or fruit flies, these sequences are variable between individuals and within individuals across grooming sessions, suggesting that flies use non-deterministic rules to make grooming decisions. Because of the complex structure of these sequences, analysis of D. melanogaster grooming can reveal distinct rules of sequence generation at different temporal scales: the long time scale of bulk behavioral progression, the intermediate scale of grooming motifs, and the short scale of individual grooming bouts.Grooming, a common behavior across species, confers social and survival benefits , 5 and pD. melanogaster grooming in response to exposure to an irritant is well-described as a process that typically progresses from an early phase characterized by preferential grooming of anterior body parts, such as eyes, to a later phase which exhibits heightened proportions of walking, abdomen cleaning, and wing cleaning. This progression occurs gradually over the course of many minutes. Previously published computational models which utilize either hierarchically structured suppression or graded sensory gain exhibit gross features of this progression, namely a gradual transition from early anterior-heavy grooming to a later quasi-steady state featuring increased posterior grooming and walking levels [On long time scales, g levels .At an intermediate temporal scale, grooming is organized in units that we refer to as motifs. We identify two classes of motifs (anterior and posterior), which are named for the set of legs used to execute them . Motifs consist of consecutive alternations between body-directed grooming bouts and leg rubbing bouts. Bouts are defined as sustained periods of a single grooming action (e.g. head cleaning or wing cleaning). Grooming bouts occur at the shortest time scale we consider here, as individual bouts typically last somewhere between 150 ms and 2 s. Flies use the same pair of legs to execute bouts within a motif, allowing them to transition between within-motif actions easily. Specifically, an anterior grooming motif consists of consecutive alternations between bouts of head cleaning and front leg rubbing. A posterior motif consists of alternating bouts of abdomen cleaning, wing cleaning, and back leg rubbing.Analysis of freely-behaving flies reveals behavioral structure at multiple time scales, with coarse-grained descriptions being sufficient to describe long-term trends and higher resolution descriptions providing increased predictive power at shorter time scales . This fiD. melanogaster as well, such as courtship and locomotion [D. melanogaster, suggesting the applicability of such models to behavioral subsets identified in their analysis, including grooming behavior.Several classes of Markov models, in which the probability of an event occurring is contingent upon previous events, have been used to describe factors involved in non-deterministic decision-making in various animals. Different Markov models vary in their structure and number of parameters but they each require a well-defined state space. Navigation and foraging have been described using Markov models, as these sequential behaviors can be decomposed into subroutines which can be categorized using easily observable dimensions such as direction and velocity –15. Thescomotion –18. Rececomotion . They ficomotion  use highIt should be noted that non-Markovian dynamics have also been identified in animal vocal sequence production, suggesting that other classes of models may be useful for describing behavioral generation –23. In fhttps://github.com/AutomaticBehaviorRecognitionSystem/ABRS/), which can classify different grooming movements from videos of flies covered in dust, to generate a large data set of ethograms—records of cleaning actions over time—from wild-type flies removing dust. We analyze more than 40 total hours of video from over 90 flies  which incorporates bout duration dependence recapitulates the observed grooming structure at long, intermediate, and short time scales. This model is partially Markovian, as transition probability matrices dictate state transition dynamics. However, the model also includes renewal process dynamics, in which the duration of the ensuing bout is determined using a random draw from the empirically observed bout duration distribution. This model is nonstationary due to the fact that Markov transition probabilities change over time. This model recapitulates both the observed bulk grooming progression statistics and the fine-grain temporal structure of grooming motifs, suggesting that sensory drive can modulate internal programs in a simple manner to prioritize different grooming strategies in different contexts.Our models clarify the syntax of grooming, demonstrating that, at the scale of individual bouts, transitions are dictated by both the identity and duration of the preceding bout. While previous studies have established the non-random nature of fruit fly grooming –18, 20, D. melanogaster that can generate patterned behavior irrespective of sensory drive, as transition rules and bout durations remain nearly constant even as the dust distribution varies over the course of grooming. Additionally, the tight correlation between body and leg-directed actions within motifs demonstrates for the first time the existence of duration dependence at an intermediate temporal scale. Finally, the model improvements provided by the inclusion of a simple nonstationarity suggest that sensory drive plays a role in guiding the implementation of internal programs over long time scales rather than serving a purely reflexive action selection function at short time scales. These discoveries provide the groundwork for future experiments and analysis of the neural underpinnings of duration-dependent action selection and sensory-driven modulation of decision-making in grooming and related behaviors.The discovery of duration dependence in grooming sequence structure suggests the existence of neural circuits in Drosophila melanogaster (N = 92) were recorded at 30 Hz for 27.8 minutes after being uniformly covered in Reactive Yellow 86 dust . To generate labeled ethograms, we apply the ABRS classifier to video (https://github.com/AutomaticBehaviorRecognitionSystem/ABRS). Following this, real time ethograms are converted to discrete bout ethograms and de-noised , resulting in two-dimensional spectra referred to as space-time images (ST-images). ST-images are subsequently modified by Radon transformation, which can be used to represent 2D shapes in a rotation and translation-invariant manner. Fast Fourier transformation is then applied to the Radon transformed ST-images, resulting in position and orientation-invariant ST-images .ST-images are then decomposed using singular value decomposition (SVD) and clustered using linear discrimination analysis (LDA) with human labeled behavioral categories. Clusters identified using LDA correspond to five predefined grooming behaviors  and two non-grooming behaviors . After classification, each video is represented by a 50,000-element vector, with each element containing a label denoting the behavior identified in the corresponding frame. Classifier performance and validation are shown in To generate discrete bout ethograms, each 50,000-element vector is transformed into a two-dimensional vector in a series of steps. The first row of this discrete bout ethogram contains the bout identity, or the label corresponding to one of the seven behaviors described above. The second row contains the bout durations, or the amount of time spent in the corresponding behavior before transitioning to another behavior.Consecutive frames containing identical actions are consolidated into one bout identity entry. Then, the corresponding bout duration entry is calculated by counting the number of consecutive identical frames and converted into seconds by dividing by the video frame rate (30 Hz). As a result, the number of entries in discrete bout ethograms corresponds to the number of observed bouts, which varies between flies. However, the sum of bout durations for each ethogram is identical (27.8 minutes).After ethogram generation, behavioral bouts persisting for less than 167 ms are deemed artifacts and deleted. This threshold is chosen because manual inspection of video data suggests that individual leg sweeps occur at approximately this time scale. This is also the approximate average transition time between behavioral states described by Berman et al. . DeletedIn order to include bout duration as a data dimension, we introduce a binning scheme. To implement this change, the continuous-valued entries in the second row of discrete bout ethograms are collapsed into duration category labels. Grooming bouts are classified into three categories  based on the duration of the associated bout. Although our results are consistent even when using more than three duration categories , we limiBin edges for these categories are determined independently for each grooming action  and walking (wk), as each action possesses a unique duration distribution. Bin edges are chosen such that each bin is populated by an equal number of samples  and are depicted in xt denotes the state probability vector at time t. The entries of xt represent the probability of the system being in the state corresponding to that entry at time t. M is the matrix of state transition probabilities such that entry Mij is the probability of transitioning from state i to state j. The vector xt will have dimensionality corresponding to the number of possible states in the system. In this case, there are either six, twelve, or eighteen grooming states, depending on whether grooming bout duration is incorporated into the model. Given sufficient data about state transitions in a real system, it is possible to generate a maximum likelihood estimate (MLE) transition probability matrix, denoted by nij is the total number of observed transitions from state i to state j. The summation over niu is a normalization factor which counts the total number of transitions from state i to any other state, indexed by u, and v denotes the total number of possible states.Markov processes are a class of probabilistic models that describe transitions between states of a system in terms of previous states. The simplest version obeys the Markov property, which declares that the next transition between states depends only on the current state. As such, long-term history dependence can be neglected when considering Markov processes, greatly simplifying the models. In discrete time, the dynamics of a first order Markov chain are described by the equationFirst order Markov chain maximum likelihood transition probabilities are determined for each ethogram as described in Statistical null models, which randomize isolated features of data while maintaining overall statistical features, provide a basis for hypothesis testing to determine the significance of results obtained from experimental data. To assess the contributions of bout identity and bout duration to sequence structure, we generate null hypotheses which independently randomize these features of the ethogram data while preserving bout duration frequency distributions within grooming action types. Null hypothesis transition probabilities are analytically tractable in the limit of infinite permutations, so we use exact formulas to calculate matrix entries . A repreDuration permuted: This null hypothesis preserves action order information, but transition matrix entries are independent of duration.Order permuted: This null hypothesis preserves action duration information, but transition matrix entries are independent of action order.n is the number of data points observed, k is the number of free model parameters, and log (L) is the maximized log-likelihood value. For first order Markov chains,i is the row index for the behavior observed at time t and j is the column index for the behavior observed at time t+1.The resulting model fit is determined using the Bayesian Information Criterion (BIC), defined asThe BIC provides a metric for evaluating model goodness-of-fit by penalizing models which use a large number of free parameters  framework. In an MRP, state transitions are governed in a manner identical to Markov chains (i.e. using a transition probability matrix). However, the amount of time spent in each state is determined in parallel by a renewal process, in which action durations are drawn from a known distribution. Here, we use the empirically observed duration distributions shown in D. melanogaster grooming begins with an initial phase of mostly front leg rubbing and head cleaning . We refer to these behaviors as anterior motif grooming actions. After roughly 13 minutes, flies reach an approximate steady state consisting of heightened amounts of abdomen cleaning, back leg rubbing, and wing cleaning . Together, these actions constitute the posterior motif. Flies also exhibit an increased proportion of walking (wk) during the late phase of grooming. Our result, visualized in After exposure to an irritant, We find that grooming bouts possess action-specific duration distributions. Interestingly, bouts durations are not normally distributed, suggesting a mechanism other than random generation of durations around a mean value. Instead, anterior motif duration distributions possess “shoulders” on each side of the central peak, suggesting that dividing actions into bout duration categories is not an unnatural distinction. Moreover, these “shoulders” are relatively broad due to the fact that grooming bouts consist of many quanta of individual leg sweeps (in the case of body-directed actions) or rubs (in the case of leg-directed actions), which are cyclical in nature and last approximately 150 ms per cycle. Due to video frame rate resolution limitations, we do not consider this extremely fast behavioral scale here. In this analysis, we highlight results which divide grooming actions into three duration categories in order to provide an intuitive demarcation between “short”, “medium”, and “long” actions. However, it is important to note that our conclusions are not strictly dependent on this choice of categorization scheme, as our results hold for higher numbers of duration categories as well We find that the vast majority of transitions occur within grooming motifs. Approximately 88% of transitions from anterior motif grooming bouts are to another anterior motif behavior, while about 70% of transitions from posterior motif bouts are to another posterior motif behavior. This tight intra-motif coupling is illustrated in We find that this strong coupling emerges from the structure of individual grooming motifs. This tight intra-motif coupling is also reflected in the structure of Markov transition matrices fit to our behavioral data (ethograms). We fit a first order discrete time Markov chain to our data and the resulting matrix, denoted by M^ , containIn Additionally, we observe that, regardless of the number of duration bins, the anterior motif transition probability structure is nearly mirror symmetrical along the diagonal (i.e. transitions between front leg rubbing and head cleaning bouts have the same probabilities). In contrast, posterior motif transitions are less strongly symmetrical, with transitions between abdomen cleaning and back leg rubbing exhibiting the highest probabilities .We find that the probabilistic rules describing transitions between consecutive bouts depend on two features of immediate behavioral history: previous bout identity and previous bout duration. To validate this observation, we evaluate the quality of the maximum likelihood model using the Bayesian Information Criterion (BIC). This metric, which is described mathematically by Inspection of BIC values indicates that bout order is the strongest individual determinant of grooming syntax and that bout duration provides an additional contribution. By construction, each null hypothesis transition matrix contains identical row structure within sets of rows corresponding to the same grooming action. However, only the duration permuted transition matrix possesses block structure similar to that found in the maximum likelihood matrix, as seen in Qualitatively, these matrices exhibit strong similarities, despite the differences in grooming proportions, as shown in Markov models are steady state models and, as such, only exhibit equilibrium dynamics when used in a generative manner to create synthetic data. The first order maximum likelihood Markov model presented above is best suited for describing the late stages of grooming, which resembles steady state behavior. This is an obvious shortcoming of using a Markovian framework for analyzing the ethograms presented here, as flies display a dynamic progression , likely In order to more explicitly model these changing sensory dynamics, we utilize a nonstationary, or time-varying, Markov renewal process (MRP). Here, we introduce a nonstationarity in the form of a time-varying transition probability matrix. In order to maintain as parsimonious a model as possible, we define the transition probability matrix as a time-dependent convex combination of an early phase matrix and a late phase matrix . The earM(t) used in the MRP is defined ast is in minutes. After 13 minutes, the late phase transition matrix is used due to the observation that flies exhibit steady state behavior after approximately that amount of time. Bout duration distributions used in the MRP do not change over time, as we did not find any significant difference between early and late bout durations . Across different binning schemes, within-motif transitions (i.e. anterior-to-anterior or posterior-to-posterior) dominate syntax at the scale of consecutive bouts, as indicated by the block-like structure of transition matrices in  of legs .Moreover, the amount of front leg rubbing over the course of the grooming progression corresponds nearly exactly to the amount of head cleaning performed by the flies. The amount of back leg rubbing is also directly proportional to the amount of abdomen and wing cleaning . Though Anterior motif transitions display symmetric transition rules, as shown in These observations support the hypothesis that alternations between anterior grooming bouts are regulated by a common source or common dynamics that dictate intra-motif transition frequency. If this is the case, future investigation of neural circuitry could focus on identifying neural activity that oscillates in phase with grooming bout alternations. Under this model, flies would then need to combine discrete decisions about which motif to perform with continuous decisions about how long to maintain a motif. This provides other targets for future experiments, as we could look for neural activity corresponding to transitions between motifs, explore manipulations that induce transitions between motifs, and search for factors that affect the duration of motifs.Although sensory input is sufficient to initiate individual grooming bouts, it has been difficult to assess the role of sensory input in grooming on longer time scales, since direct quantification of irritant relies on invasive protocols . To dateHere, we abstract the contribution of sensory input, incorporating it as a parameter in a time-varying Markov renewal process. Specifically, we approximate changing sensory conditions as a linear phenomenon which dictates the degree to which flies favor early versus late grooming rules . This chD. melanogaster instead use sensory information to modulate internal grooming programs. This type of slowly varying process has been observed in other organisms, suggesting that it is a useful framework for describing sequential behaviors [Since our MRP reliably reproduces true ethogram statistics without explicitly modeling sensory input, we propose that, rather than acting in a purely reflexive manner, ehaviors . AdditioFrom a computational perspective, sensing is costly when it requires high-frequency updates and can provide readouts at a high resolution, as specialized neural circuits must be formed, maintained, and integrated in order to provide accurate, real-time readouts. This can lead to increased metabolic costs on several fronts, as increased neural activity and behaviors intended to protect and maintain sensory machinery can also require energetic resources . In contD. melanogaster utilize sensory information to dictate decisions about which grooming motif to perform, since the dust distribution changes over the period of cleaning.During the execution of a grooming motif, frequent, high-resolution sensory updates may not be necessary. Instead, the moment-to-moment decisions about execution of grooming bouts can be automated by utilizing patterned internal dynamics. Several models of behavioral generation in which internal triggers drive sequence generation have been proposed , 29. SinDrosophila, our results suggest that such neuronal patterns may exist: perhaps by combining descending commands from the brain with local circuitry within and emerging from the ventral nerve cord.” This observation is consistent with our interpretation of our results, as local circuitry in the ventral nerve cord could plausibly generate internal dynamics which introduce duration dependence to action selection.In discussing temporal correlations between neural activity across spatial scales, Berman et al.  note thaDrosophila melanogaster nervous systems may utilize multi-level control algorithms which make discrete, “ballistic” decisions about the onset and type of continuous behaviors, allowing them to update infrequently at low sensory resolution. Once the decision is made, the execution of the behavior can be guided primarily by autonomous internal dynamics. In flies, these internal dynamics may not be unique to grooming, as freely-behaving D. melanogaster exhibit hierarchical Markov-like behavior in the absence of external sensory stimuli [D. melanogaster locomotion further supports the idea that specialized neural circuits can automate and reproduce portions of behavior, reducing the need for constant calibration via sensory feedback [By combining sensory input and internal dynamics,  stimuli , 18. Addfeedback . We can D. melanogaster, specialized, isolated neural circuits known as central pattern generators (CPGs), use rhythmic activity to guide execution of locomotion and courtship song sequences [In larval and mature equences , 31. In equences , 33, indSince CPGs can modulate motor programs independently of sensory input, they are a natural candidate to execute grooming behavior on the scale of individual leg sweeps and rubs within a grooming bout. We do not explicitly model behavior at this temporal scale, but the ABRS classifier applied to video at the frame rate used here identifies actions nearly at the temporal resolution of individual leg movements. We plan to extend analysis to this time scale in future work.D. melanogaster grooming behavior as well, leaving open a role for internal regulation of sequence generation at longer time scales [Additionally, robotic systems that utilize multi-layer CPG architectures can carry out locomotor tasks, indicating that hierarchical CPG structures can generate sequential behaviors from smaller elements . Recent e scales .When describing complex behavioral phenomena, formal mathematical models can bridge the gap between phenomenology and biological mechanisms by providing parameters that may correspond to underlying neuronal activity. In many cases, statistical model parameters do not directly describe to underlying biological structures, but data analysis can suggest hypotheses for future exploration.D. melanogaster grooming. Using Markov models to detect features of grooming sequences, we find that grooming actions exhibit duration dependence at the scale of individual bouts and grooming motifs. Then, we produce realistic synthetic ethograms by introducing a slow modulation of grooming rules meant to abstract the contribution of sensory input. Together, these findings suggest that internal programs dictate grooming decision on fast time scales but are modulated by sensory input over longer time scales.Here, we analyze large-scale automatically annotated data sets to reveal the syntax of As the tools for computational analysis of behavioral data continue to develop, interdisciplinary approaches that use mathematical tools to illuminate biological mechanisms will yield further insight into the generation of sequential behavior. Future experiments involving the manipulation of sensory experiences using optogenetic stimulation or other methods will also allow researchers to test hypotheses with unprecedented precision and scope. Together, these advances promise to improve our understanding of sequence generation by connecting mathematical descriptions of behavior with the underlying neural circuitry, as we are now motivated to search for circuits controlling long, intermediate, and short time scale grooming rules and determine how they interact with sensory circuits. Finally, our work suggests that temporal dynamics can and should be included when using statistical models to assess complex behaviors.S1 FigA: Here, grooming behaviors are visualized in low-dimensional space using t-SNE, which preserves local distances between nearby points. Color indicates behavior type. Behaviors are well-separable by type. B: Example classifications of ethograms of grooming behaviors are shown. Human-algorithm agreement is about 75% (in terms of number of matching frames).(TIF)Click here for additional data file.S2 FigWhen transition probabilities are calculated from ethograms with 4 (left) or 5 (right) duration categories, fine-grain temporal structure is still present (compare the matrices shown here to those shown in (TIF)Click here for additional data file.S3 FigTwo examples of individual fly transition probabilities illustrate that, although syntax varies between individuals and over time, individuals are relatively stable and resemble the group average (compare to (TIF)Click here for additional data file.S4 FigPlotted here is the relationship between consecutive anterior grooming action durations. Linear regression produces a poor fit, indicating that duration dependence is not linear.(TIF)Click here for additional data file.S5 FigTo ensure that our choice of boundary between early and late phases does not alter our results, we partition our data in a stricter manner so that the transition period which occurs during the middle of the grooming progression is excluded. Partitioning the data into thirds produces transition matrices with structure and stationarity similar to those shown in (TIF)Click here for additional data file.S6 FigWe find that 253 of the 266 (95.1%) non-zero entries change by less than 5% and 168 (63.2%) entries change by less than 1%. This was a very surprising finding and suggests that, although some non-stationarity does exist, it is much smaller than we had expected given that the average time spent performing different behaviors varies between the early and late phases.(TIF)Click here for additional data file.S7 FigProbability density plots of bout duration distributions illustrate that bout durations remain consistent between early and late phases of grooming. Early phase data is plotted with a thick line. Late phase data is plotted with a thin line.(TIF)Click here for additional data file.S1 TableMij, for each null hypothesis transition matrix. Here, i denotes the row and j denotes the column of M. Note that, in the three duration category case, each state has both an action and duration identifier (e.g. row h.s. corresponds to a head cleaning action in the short duration category). P is the absolute probability of transitioning from the grooming action represented by row i to the action represented by row j, regardless of duration  is the absolute probability of the grooming action represented by row j belonging to the duration category represented by row j. P(xj) is the absolute probability of state j.Given in S1 Table are the analytic formulas for transition matrix entries, (TIF)Click here for additional data file.