Pub Date : 2024-09-18DOI: 10.1101/2024.09.17.24313638
khaoula Ben Messaoud, Vahe Khachadourian, Elias Arildskov, Stefan Hansen, Renee Gardner, Cecilia Ramlau-Hansen, Linda Kahn, Magdalena Janecka
IMPORTANCE Existing research suggests the impact of infertility on the risk of neurodevelopmental disorders in children, however, studies to date have failed to separate the impact of male and female infertility, often blurring the lines with proxies that encompass all forms of infertility. Moreover, while both health conditions co-occurring with infertility and genetic factors operating upstream have been suggested to influence the association between infertility and child outcomes, their roles and potential impact on observed associations remain unclear. OBJECTIVE The objectives of this study are to investigate the relationship between female infertility and autism in the child, differentiating it from the effects of male and the couple infertility; consider the role of various maternal and birth factors in the association; and examine the effects of shared familial confounders on the association. DESIGN SETTING AND PARTICIPANTS Danish population-based cohort study, including all singleton live births in Denmark 1998-2015, their parents and parents' siblings. The cohort was followed up until December 31, 2016. EXPOSURES The exposure was a history of female infertility in the mother and the mother's sister. We examined four definitions of female infertility based on the ICD-10 codes derived from the Danish National Patient Register - any female infertility; specified female infertility; female exclusive infertility; and female or male infertility. MAIN OUTCOME AND MEASURES The outcome was diagnosis of autism spectrum disorder (ASD) in the Danish Psychiatric Central Research Register or the national patient register. A multivariable Cox regression model was used to estimate the associations between female infertility and autism, accounting for child's sex, year of birth, maternal age, education level, chronic comorbidities, and pregnancy and birth complications. The effects of shared familial factors on the association were analyzed using exposure information from the child's maternal aunt. RESULTS The cohort included 1,131,899 mother-child pairs, among which 18,374 children with ASD diagnosis. History of female infertility in the mother (all definitions) was significantly associated with autism in the child, with the association remaining robust after adjustment for covariates (HRadj=1.14 (95% CI, 1.03-1.26) for specified infertility). The diagnosis of infertility in a child's maternal aunt was also significantly linked to the child's autism risk, even after adjustment for maternal infertility (HRadj=1.10 (95% CI, 1.00-1.20). CONCLUSIONS AND RELEVANCE in This population-based birth cohort study, we found a slightly higher risk of autism in children born to mothers with a history of infertility, with the association remaining consistent across various definitions of female infertility and robust to adjustments for demographic, child, and maternal factors. The study suggests for the first time that shared familial factors, possibly both genetic an
{"title":"Female Infertility and Neurodevelopmental Disorders in Children: associations and evidence for familial confounding in Denmark","authors":"khaoula Ben Messaoud, Vahe Khachadourian, Elias Arildskov, Stefan Hansen, Renee Gardner, Cecilia Ramlau-Hansen, Linda Kahn, Magdalena Janecka","doi":"10.1101/2024.09.17.24313638","DOIUrl":"https://doi.org/10.1101/2024.09.17.24313638","url":null,"abstract":"IMPORTANCE Existing research suggests the impact of infertility on the risk of neurodevelopmental disorders in children, however, studies to date have failed to separate the impact of male and female infertility, often blurring the lines with proxies that encompass all forms of infertility. Moreover, while both health conditions co-occurring with infertility and genetic factors operating upstream have been suggested to influence the association between infertility and child outcomes, their roles and potential impact on observed associations remain unclear.\u0000OBJECTIVE The objectives of this study are to investigate the relationship between female infertility and autism in the child, differentiating it from the effects of male and the couple infertility; consider the role of various maternal and birth factors in the association; and examine the effects of shared familial confounders on the association.\u0000DESIGN SETTING AND PARTICIPANTS Danish population-based cohort study, including all singleton live births in Denmark 1998-2015, their parents and parents' siblings. The cohort was followed up until December 31, 2016.\u0000EXPOSURES The exposure was a history of female infertility in the mother and the mother's sister. We examined four definitions of female infertility based on the ICD-10 codes derived from the Danish National Patient Register - any female infertility; specified female infertility; female exclusive infertility; and female or male infertility. MAIN OUTCOME AND MEASURES The outcome was diagnosis of autism spectrum disorder (ASD) in the Danish Psychiatric Central Research Register or the national patient register. A multivariable Cox regression model was used to estimate the associations between female infertility and autism, accounting for child's sex, year of birth, maternal age, education level, chronic comorbidities, and pregnancy and birth complications. The effects of shared familial factors on the association were analyzed using exposure information from the child's maternal aunt.\u0000RESULTS The cohort included 1,131,899 mother-child pairs, among which 18,374 children with ASD diagnosis. History of female infertility in the mother (all definitions) was significantly associated with autism in the child, with the association remaining robust after adjustment for covariates (HRadj=1.14 (95% CI, 1.03-1.26) for specified infertility). The diagnosis of infertility in a child's maternal aunt was also significantly linked to the child's autism risk, even after adjustment for maternal infertility (HRadj=1.10 (95% CI, 1.00-1.20).\u0000CONCLUSIONS AND RELEVANCE in This population-based birth cohort study, we found a slightly higher risk of autism in children born to mothers with a history of infertility, with the association remaining consistent across various definitions of female infertility and robust to adjustments for demographic, child, and maternal factors. The study suggests for the first time that shared familial factors, possibly both genetic an","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1101/2024.09.16.24313623
David L Smith, Daniel J Laydon, Kaustubh Chakradeo, Mark P Khurana, Jaffer Okiring, David A Duchene, Samir Bhatt
Mosquito ecology and malaria parasite development in mosquitoes display marked sensitivity to weather, in particular to temperature and precipitation. Therefore, climate change is expected to profoundly affect malaria epidemiology in its transmission, spatiotemporal distribution and consequent disease burden. However, malaria transmission is also complicated by other factors (e.g. urbanisation, economics, genetics, drug resistance) which together constitute a highly complex, dynamical system, where the influence of any single factor is highly uncertain. In this study, we therefore aim to re-evaluate the evidence underlying the widespread belief that climate change will increase worldwide malaria transmission. We firstly review the different types of studies that have contributed to this evidence-base: i) studies that project changes in transmission due to inferred relationships between environmental and mosquito entomology; ii) studies that focus on either mechanistic transmission or time series models to estimate the effects of malaria control strategies on prevalence, and iii) regression-based studies that look for associations between environmental variables and malaria prevalence. We then employ a simple statistical model to show that environmental variables alone do not account for the observed spatiotemporal variation in malaria prevalence. Our review raises several concerns about the robustness of the analyses used for advocacy around climate change and malaria. We find that, while climate change's effect on malaria is highly plausible, empirical evidence is much less certain. Future research on climate change and malaria must become integrated into malaria control programs, and understood in context as one factor among many affecting malaria. Our work outlines gaps in modelling that we believe are priorities for future research.
{"title":"Climate Change and Malaria: A Call for Robust Analytics","authors":"David L Smith, Daniel J Laydon, Kaustubh Chakradeo, Mark P Khurana, Jaffer Okiring, David A Duchene, Samir Bhatt","doi":"10.1101/2024.09.16.24313623","DOIUrl":"https://doi.org/10.1101/2024.09.16.24313623","url":null,"abstract":"Mosquito ecology and malaria parasite development in mosquitoes display marked sensitivity to weather, in particular to temperature and precipitation. Therefore, climate change is expected to profoundly affect malaria epidemiology in its transmission, spatiotemporal distribution and consequent disease burden. However, malaria transmission is also complicated by other factors (e.g. urbanisation, economics, genetics, drug resistance) which together constitute a highly complex, dynamical system, where the influence of any single factor is highly uncertain. In this study, we therefore aim to re-evaluate the evidence underlying the widespread belief that climate change will increase worldwide malaria transmission. We firstly review the different types of studies that have contributed to this evidence-base: i) studies that project changes in transmission due to inferred relationships between environmental and mosquito entomology; ii) studies that focus on either mechanistic transmission or time series models to estimate the effects of malaria control strategies on prevalence, and iii) regression-based studies that look for associations between environmental variables and malaria prevalence. We then employ a simple statistical model to show that environmental variables alone do not account for the observed spatiotemporal variation in malaria prevalence. Our review raises several concerns about the robustness of the analyses used for advocacy around climate change and malaria. We find that, while climate change's effect on malaria is highly plausible, empirical evidence is much less certain. Future research on climate change and malaria must become integrated into malaria control programs, and understood in context as one factor among many affecting malaria. Our work outlines gaps in modelling that we believe are priorities for future research.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1101/2024.09.14.24313681
Mike Javan Mwanga, Laura M Guzman-Rincon, Leonard Kingwara, Don B Odhiambo, Henry Gathuri, Arnold W. Lambisia, John Mwita Morobe, Edidah Moraa, Bernadette Kutima, John Gitonga, Daisy Mugo, Charles N Agoti, James Nyagwange, George M Warimwe, Isabella Oyier, D James Nokes, Ambrose Agweyu, E Wangeci Kagucia, Anthony O Etyang, John N. Kiiru, George Githinji
A multi-pronged approach to combating the COVID-19 pandemic in Kenya resulted in the formation of multidisciplinary research initiatives including genomic sequencing, syndromic surveillance, sero-surveillance, vaccination, and mathematical modelling. These initiatives generated an overwhelming amount of data that posed a challenge to researchers and public health officials, to effectively manage, analyse and promptly interpret for immediate pandemic response. As a result, there was demand for a platform to collate and integrate these datasets with interpretable findings to aid in pandemic management. In response, we developed a web-based dashboard, and integrated multidisciplinary datasets collected by the Ministry of Health-Kenya (MoH-K) and other research organizations, to support surveillance and monitoring of COVID-19 in Kenya. The developed dashboard combines genomics, epidemiological, seroprevalence, modelling, vaccination, syndromic and phylogenetic data and provides real-time updates to the public and health sector experts. The dashboard provides temporal trends of reported COVID-19 cases, fatalities, variants, and vaccination, in addition to summary reports from multiple cross-sectional seroprevalence studies and ongoing facility-based inpatient syndromic surveillance from 15 health facilities across Kenya. This is the first detailed interactive dashboard in Kenya that combines multiple datasets from a disease outbreak to provide valuable insights to researchers, health policy makers, the media and public not only during pandemic but also during routine surveillance. This resource is a model for digital platform for infectious disease surveillance and for informing public health planning and intervention.
{"title":"Leveraging an Online Dashboard to Inform on Infectious Disease Surveillance: A case Study of COVID-19 in Kenya.","authors":"Mike Javan Mwanga, Laura M Guzman-Rincon, Leonard Kingwara, Don B Odhiambo, Henry Gathuri, Arnold W. Lambisia, John Mwita Morobe, Edidah Moraa, Bernadette Kutima, John Gitonga, Daisy Mugo, Charles N Agoti, James Nyagwange, George M Warimwe, Isabella Oyier, D James Nokes, Ambrose Agweyu, E Wangeci Kagucia, Anthony O Etyang, John N. Kiiru, George Githinji","doi":"10.1101/2024.09.14.24313681","DOIUrl":"https://doi.org/10.1101/2024.09.14.24313681","url":null,"abstract":"A multi-pronged approach to combating the COVID-19 pandemic in Kenya resulted in the formation of multidisciplinary research initiatives including genomic sequencing, syndromic surveillance, sero-surveillance, vaccination, and mathematical modelling. These initiatives generated an overwhelming amount of data that posed a challenge to researchers and public health officials, to effectively manage, analyse and promptly interpret for immediate pandemic response. As a result, there was demand for a platform to collate and integrate these datasets with interpretable findings to aid in pandemic management. In response, we developed a web-based dashboard, and integrated multidisciplinary datasets collected by the Ministry of Health-Kenya (MoH-K) and other research organizations, to support surveillance and monitoring of COVID-19 in Kenya. The developed dashboard combines genomics, epidemiological, seroprevalence, modelling, vaccination, syndromic and phylogenetic data and provides real-time updates to the public and health sector experts. The dashboard provides temporal trends of reported COVID-19 cases, fatalities, variants, and vaccination, in addition to summary reports from multiple cross-sectional seroprevalence studies and ongoing facility-based inpatient syndromic surveillance from 15 health facilities across Kenya. This is the first detailed interactive dashboard in Kenya that combines multiple datasets from a disease outbreak to provide valuable insights to researchers, health policy makers, the media and public not only during pandemic but also during routine surveillance. This resource is a model for digital platform for infectious disease surveillance and for informing public health planning and intervention.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1101/2024.09.17.24313808
Dennison Kizito, Sam Tweed, Joseph Mutyaba, Nackson Babi, Swaib Lule, Gladys Nakanjako Kiggundu, Rory Gibb, Charity Angella Nassuna, Ronald Ssali Ogwal, Ebenezer Paul, Mercy Haumba, Collins Agaba, Phionah Katushabe, Eric Morris Enyel, Stephen Balinandi, Lydia Franklinos, Naomi Fuller, Leah Owen, Laura Ferguson, Deo Birungi Ndumu, Musa Sekammatte, Atimnedi Patrick, Luke Nyakarahuka, Gladys Kalema-Zikusoka, Ibrahim Abubakar, Nigel Field, Janet Seeley, Julius Julian Lutwama, COHRIE-Uganda Team
Background Zoonoses are of public health importance with most major emerging diseases originating in animal populations. Rift Valley Fever (RVF), Crimean-Congo Haemorrhagic Fever (CCHF) and Brucellosis are circulating in Uganda causing frequent outbreaks, but gaps exist in the understanding of transmission dynamics, community perspectives and effective mitigation strategies of these diseases. With increasing human-livestock-wildlife interaction in Uganda's biodiverse cattle corridor, this study protocol outlines an integrated One Health model to determine the burden of RVF, CCHF and Brucellosis, identify key vectors and reservoirs and assesses the impact of social and policy factors on disease distribution. Methods A series of mixed-methods cross-sectional and longitudinal surveys across six conservation areas experiencing high human-livestock-wildlife interaction spanning Uganda's Cattle Corridor: Queen Elizabeth National Park, Bwindi-Mgahinga Impenetrable Forest, Lake Mburo-Nakivaale, Murchison Falls, Kidepo Valley and Pian Upe Game Reserve. In selected villages household surveys comprise questionnaires, focus-group discussions and in-depth interviews to determine drivers of disease risk with blood-sampling of human population. Questionnaires provide detail on livestock practices and blood-sampling is conducted on cattle, sheep, pigs and goats. Targeted sampling of vectors in these localities including mosquitos, ticks and small mammals using environmental traps and on-host collection. Specimens taken from nearby large wildlife include blood sampling and nasal swabs. Serological testing using indirect ELISA and molecular testing using real-time PCR was conducted to determine disease status of RVF, CCHF and Brucellosis across humans, livestock and wildlife with eco-epi modelling and qualitative analyses used to inform risks and drivers of disease. Results Baseline survey data and blood specimens were obtained from 2894 humans residing in 1602 households across 96 villages in 6 conservation areas of Uganda. A further 379 community members participated in focus group discussions and key informant interviews with 978 reformed and active poachers across 4 conservation areas. 3692 livestock were sampled including 1925 cattle, 1409 goats, 282 sheep and 76 pigs from 358 herds. Vector data were collected for 18236 ticks, 53480 mosquitoes and 768 rodents. 241 large wildlife were sampled including buffalo, kobs, zebras, waterbucks, topi and hartebeest. 127 Community One Health Volunteers (COHVs) were enlisted to monitor and detect outbreaks in the study sites. Conclusions This paper outlines a comprehensive One Health approach to studying neglected zoonotic diseases integrating molecular epidemiology, social sciences and community participatory approaches involving humans, livestock, vectors and wildlife across 6 conservation areas in Uganda. It will inform interventions to enhance the surveillance and control of RVF, CCHF and Brucellosis including streng
{"title":"Surveillance and control of neglected zoonotic diseases: methodological approaches to studying Rift Valley Fever, Crimean-Congo Haemorrhagic Fever and Brucellosis at the human-livestock-wildlife interface across diverse agricultural systems in Uganda","authors":"Dennison Kizito, Sam Tweed, Joseph Mutyaba, Nackson Babi, Swaib Lule, Gladys Nakanjako Kiggundu, Rory Gibb, Charity Angella Nassuna, Ronald Ssali Ogwal, Ebenezer Paul, Mercy Haumba, Collins Agaba, Phionah Katushabe, Eric Morris Enyel, Stephen Balinandi, Lydia Franklinos, Naomi Fuller, Leah Owen, Laura Ferguson, Deo Birungi Ndumu, Musa Sekammatte, Atimnedi Patrick, Luke Nyakarahuka, Gladys Kalema-Zikusoka, Ibrahim Abubakar, Nigel Field, Janet Seeley, Julius Julian Lutwama, COHRIE-Uganda Team","doi":"10.1101/2024.09.17.24313808","DOIUrl":"https://doi.org/10.1101/2024.09.17.24313808","url":null,"abstract":"Background Zoonoses are of public health importance with most major emerging diseases originating in animal populations. Rift Valley Fever (RVF), Crimean-Congo Haemorrhagic Fever (CCHF) and Brucellosis are circulating in Uganda causing frequent outbreaks, but gaps exist in the understanding of transmission dynamics, community perspectives and effective mitigation strategies of these diseases. With increasing human-livestock-wildlife interaction in Uganda's biodiverse cattle corridor, this study protocol outlines an integrated One Health model to determine the burden of RVF, CCHF and Brucellosis, identify key vectors and reservoirs and assesses the impact of social and policy factors on disease distribution. Methods A series of mixed-methods cross-sectional and longitudinal surveys across six conservation areas experiencing high human-livestock-wildlife interaction spanning Uganda's Cattle Corridor: Queen Elizabeth National Park, Bwindi-Mgahinga Impenetrable Forest, Lake Mburo-Nakivaale, Murchison Falls, Kidepo Valley and Pian Upe Game Reserve. In selected villages household surveys comprise questionnaires, focus-group discussions and in-depth interviews to determine drivers of disease risk with blood-sampling of human population. Questionnaires provide detail on livestock practices and blood-sampling is conducted on cattle, sheep, pigs and goats. Targeted sampling of vectors in these localities including mosquitos, ticks and small mammals using environmental traps and on-host collection. Specimens taken from nearby large wildlife include blood sampling and nasal swabs. Serological testing using indirect ELISA and molecular testing using real-time PCR was conducted to determine disease status of RVF, CCHF and Brucellosis across humans, livestock and wildlife with eco-epi modelling and qualitative analyses used to inform risks and drivers of disease. Results Baseline survey data and blood specimens were obtained from 2894 humans residing in 1602 households across 96 villages in 6 conservation areas of Uganda. A further 379 community members participated in focus group discussions and key informant interviews with 978 reformed and active poachers across 4 conservation areas. 3692 livestock were sampled including 1925 cattle, 1409 goats, 282 sheep and 76 pigs from 358 herds. Vector data were collected for 18236 ticks, 53480 mosquitoes and 768 rodents. 241 large wildlife were sampled including buffalo, kobs, zebras, waterbucks, topi and hartebeest. 127 Community One Health Volunteers (COHVs) were enlisted to monitor and detect outbreaks in the study sites. Conclusions This paper outlines a comprehensive One Health approach to studying neglected zoonotic diseases integrating molecular epidemiology, social sciences and community participatory approaches involving humans, livestock, vectors and wildlife across 6 conservation areas in Uganda. It will inform interventions to enhance the surveillance and control of RVF, CCHF and Brucellosis including streng","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-17DOI: 10.1101/2024.09.17.24313793
Emilie Finch, Adam J Kucharski, Shuzhen Sim, Lee Ching Ng, Rachel Lowe
Dengue poses a rapidly increasing threat to global health, with Southeast Asia as one of the worst affected regions. Climate-informed early warning systems can help to mitigate the impact of outbreaks; however, prediction of large outbreaks with sufficient lead time to guide interventions remains a challenge. In this work, we quantify the role of climatic variation and serotype competition in shaping dengue risk in Singapore using over 20 years of weekly case data. We integrated these findings into an early warning system framework able to predict dengue outbreaks up to 2 months ahead. While a climate-informed model improved predictive power by 54% compared to a seasonal baseline, including additional serotype information increased predictive performance to 60%, helping to explain interannual variation. By incorporating serotype competition as a proxy for population immunity, this work advances the field of dengue prediction and demonstrates the value of long-term virus surveillance.
{"title":"Climate variation and serotype competition drive dengue outbreak dynamics in Singapore","authors":"Emilie Finch, Adam J Kucharski, Shuzhen Sim, Lee Ching Ng, Rachel Lowe","doi":"10.1101/2024.09.17.24313793","DOIUrl":"https://doi.org/10.1101/2024.09.17.24313793","url":null,"abstract":"Dengue poses a rapidly increasing threat to global health, with Southeast Asia as one of the worst affected regions. Climate-informed early warning systems can help to mitigate the impact of outbreaks; however, prediction of large outbreaks with sufficient lead time to guide interventions remains a challenge. In this work, we quantify the role of climatic variation and serotype competition in shaping dengue risk in Singapore using over 20 years of weekly case data. We integrated these findings into an early warning system framework able to predict dengue outbreaks up to 2 months ahead. While a climate-informed model improved predictive power by 54% compared to a seasonal baseline, including additional serotype information increased predictive performance to 60%, helping to explain interannual variation. By incorporating serotype competition as a proxy for population immunity, this work advances the field of dengue prediction and demonstrates the value of long-term virus surveillance.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background Persons with disabilities should require the same level of access to contraception as the general population. However, the extent of contraception use among this group is underexplored in low- and middle-income countries (LMICs). Objective: This study aimed to determine the prevalence of modern contraception use among persons with disabilities in LMICs and identify the key determinants. Methods In June 2024, we conducted a systematic search across six databases to identify studies on disability and modern contraception in LMICs. The primary outcomes were the prevalence of modern contraception use and its determinants among persons with disabilities. Summary estimates were calculated using fixed or random-effects meta-analysis, depending on the level of heterogeneity. Results A total of 19 studies were identified, with 11 included in the meta-analysis. The pooled prevalence of modern contraception use among persons with disabilities was 31.4% (95% CI: 26.5, 36.2), with significant heterogeneity across respondent characteristics. Five factors were significantly associated with higher contraception use: age over 25 years, having some level of education, being in a higher wealth quintile, adequate knowledge of family planning, and being in a formal marital relationship. Conclusion This study reveals a significantly lower prevalence of modern contraception use among persons with disabilities in LMICs. Improving access to education, addressing social norms, and strengthening healthcare systems may contribute to increase contraception access and uptake among persons with disabilities in LMICs.
{"title":"Prevalence and determinants of modern contraception use among persons with disabilities in low- and middle-income countries: a systematic review and meta-analysis","authors":"Atika Rahman Chowdhury, Shimlin Jahan Khanam, Mohammad Zahidul Islam, Gulam Khandaker, Md Nuruzzaman Khan","doi":"10.1101/2024.09.13.24313669","DOIUrl":"https://doi.org/10.1101/2024.09.13.24313669","url":null,"abstract":"Background\u0000Persons with disabilities should require the same level of access to contraception as the general population. However, the extent of contraception use among this group is underexplored in low- and middle-income countries (LMICs). Objective: This study aimed to determine the prevalence of modern contraception use among persons with disabilities in LMICs and identify the key determinants.\u0000Methods\u0000In June 2024, we conducted a systematic search across six databases to identify studies on disability and modern contraception in LMICs. The primary outcomes were the prevalence of modern contraception use and its determinants among persons with disabilities. Summary estimates were calculated using fixed or random-effects meta-analysis, depending on the level of heterogeneity.\u0000Results\u0000A total of 19 studies were identified, with 11 included in the meta-analysis. The pooled prevalence of modern contraception use among persons with disabilities was 31.4% (95% CI: 26.5, 36.2), with significant heterogeneity across respondent characteristics. Five factors were significantly associated with higher contraception use: age over 25 years, having some level of education, being in a higher wealth quintile, adequate knowledge of family planning, and being in a formal marital relationship.\u0000Conclusion\u0000This study reveals a significantly lower prevalence of modern contraception use among persons with disabilities in LMICs. Improving access to education, addressing social norms, and strengthening healthcare systems may contribute to increase contraception access and uptake among persons with disabilities in LMICs.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-15DOI: 10.1101/2024.09.13.24313667
Atchuta Srinivas Duddu, Islam Elgamal, José Camacho-Mateu, Olena Holubowska, Simon A. Rella, Samantha J. Bents, Cécile Viboud, Chelsea L. Hansen, Giulia Pullano, Amanda C. Perofsky
Introduction: Respiratory Syncytial Virus (RSV) infection is a major cause of acute respiratory hospitalizations in young children and older adults. In early 2020 most countries implemented non-pharmaceutical interventions (NPIs) to slow the spread of SARS-CoV-2. COVID-19 NPIs disrupted the transmission of RSV on a global scale, and many locations did not experience widespread re-circulation until late 2020 or 2021. Here, we use a mechanistic transmission model informed by cellphone mobility data to determine which aspects of population behavior had the greatest influence on post-pandemic RSV rebound in Seattle, Washington. Methods: We used aggregated mobile device location data to characterize within-city mixing, visitor in-flows, and foot traffic to points of interest in Seattle. We fit an age-structured epidemiological model to data on weekly RSV hospitalizations, allowing for reductions in transmission due to declines in mobility during the pandemic. We compared model fits to observed data to assess which mobility behaviors best capture RSV dynamics during the first two post-pandemic waves in Seattle. Results: In Seattle, COVID-19 NPIs perturbed RSV seasonality from 2020 to 2022. Seattle experienced a small out-of-season outbreak in Summer 2021 and an atypically large and early wave in Fall 2022. RSV transmission models incorporating mobility network connectivity (measured as the average shortest path length between Seattle neighborhoods) or the inflow of visitors from outside of Seattle best captured the timing and magnitude of the first two post-pandemic waves. Models including foot traffic to schools or child daycares or within-neighborhood movement produced poor fits to observed data. Conclusions: Our results suggest that case importations from other regions and local spread between neighborhoods had the greatest influence on the timing of RSV reemergence in Seattle. These findings contribute to the understanding of behavioral factors underlying RSV epidemic spread and can inform the timing of preventative measures, such as the administration of immunoprophylaxis.
{"title":"Modeling the effects of COVID-19 mobility disruptions on RSV transmission in Seattle, Washington","authors":"Atchuta Srinivas Duddu, Islam Elgamal, José Camacho-Mateu, Olena Holubowska, Simon A. Rella, Samantha J. Bents, Cécile Viboud, Chelsea L. Hansen, Giulia Pullano, Amanda C. Perofsky","doi":"10.1101/2024.09.13.24313667","DOIUrl":"https://doi.org/10.1101/2024.09.13.24313667","url":null,"abstract":"<strong>Introduction:</strong> Respiratory Syncytial Virus (RSV) infection is a major cause of acute respiratory hospitalizations in young children and older adults. In early 2020 most countries implemented non-pharmaceutical interventions (NPIs) to slow the spread of SARS-CoV-2. COVID-19 NPIs disrupted the transmission of RSV on a global scale, and many locations did not experience widespread re-circulation until late 2020 or 2021. Here, we use a mechanistic transmission model informed by cellphone mobility data to determine which aspects of population behavior had the greatest influence on post-pandemic RSV rebound in Seattle, Washington.\u0000<strong>Methods:</strong> We used aggregated mobile device location data to characterize within-city mixing, visitor in-flows, and foot traffic to points of interest in Seattle. We fit an age-structured epidemiological model to data on weekly RSV hospitalizations, allowing for reductions in transmission due to declines in mobility during the pandemic. We compared model fits to observed data to assess which mobility behaviors best capture RSV dynamics during the first two post-pandemic waves in Seattle.\u0000<strong>Results:</strong> In Seattle, COVID-19 NPIs perturbed RSV seasonality from 2020 to 2022. Seattle experienced a small out-of-season outbreak in Summer 2021 and an atypically large and early wave in Fall 2022. RSV transmission models incorporating mobility network connectivity (measured as the average shortest path length between Seattle neighborhoods) or the inflow of visitors from outside of Seattle best captured the timing and magnitude of the first two post-pandemic waves. Models including foot traffic to schools or child daycares or within-neighborhood movement produced poor fits to observed data.\u0000<strong>Conclusions:</strong> Our results suggest that case importations from other regions and local spread between neighborhoods had the greatest influence on the timing of RSV reemergence in Seattle. These findings contribute to the understanding of behavioral factors underlying RSV epidemic spread and can inform the timing of preventative measures, such as the administration of immunoprophylaxis.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269384","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1101/2024.09.13.24313615
Prathith Bhargav, Soumil Kelkar, Joy Merwin Monteiro, Philip Cherian
Routine epidemiological surveillance data represents one of the most continuous and comprehensive sources of data during the course of an epidemic. This data is used as inputs to epidemiological forecasting models as well as for public health decision making such as imposition and lifting of lockdowns and quarantine measures. However, such data is generated during testing and contact tracing and not through randomized sampling which makes it unclear how representative such data is of the epidemic itself. Using the BharatSim simulation framework, we build an agent-based epidemiological model with a detailed algorithm of testing and contact tracing representative of actual strategies employed in Pune city to generate synthetic surveillance data. We simulate the impact of different public health strategies, availability of tests and contact tracing efficiencies on the resulting surveillance data as well as on the course of the epidemic. The fidelity of the resulting surveillance data in representing the real-time state of the epidemic and in decision-making is explored in the context of Pune city.
{"title":"Interpreting epidemiological surveillance data: A modelling study from Pune City","authors":"Prathith Bhargav, Soumil Kelkar, Joy Merwin Monteiro, Philip Cherian","doi":"10.1101/2024.09.13.24313615","DOIUrl":"https://doi.org/10.1101/2024.09.13.24313615","url":null,"abstract":"Routine epidemiological surveillance data represents one of the most continuous and comprehensive sources of data during the course of an epidemic. This data is used as inputs to epidemiological forecasting models as well as for public health decision making such as imposition and lifting of lockdowns and quarantine measures. However, such data is generated during testing and contact tracing and not through randomized sampling which makes it unclear how representative such data is of the epidemic itself. Using the BharatSim simulation framework, we build an agent-based epidemiological model with a detailed algorithm of testing and contact tracing representative of actual strategies employed in Pune city to generate synthetic surveillance data. We simulate the impact of different public health strategies, availability of tests and contact tracing efficiencies on the resulting surveillance data as well as on the course of the epidemic. The fidelity of the resulting surveillance data in representing the real-time state of the epidemic and in decision-making is explored in the context of Pune city.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1101/2024.09.13.24312295
Nicholas C Peiper, Stephen Furmanek, Kelly C McCants, Edward Hoyt Brown
Background: Clinical trials are critical to scientific advances and medical progress, although awareness and participation remain low in general populations. The existing literature indicates that clinical trial knowledge and participation is multifactorial. Yet, little is known about the association between clinical trial participation with health technology use and digital health engagement to search for health information, interact with medical providers, and seek health supports. Objective: Examine the multivariate association between clinical trial knowledge and participation with past-year health technology use and digital health engagement with medical providers. Design: Cross-sectional data from a federal surveillance system. Participants: A total of 3,865 US adult respondents from the Health Information National Trends Survey 5, Cycle 4 conducted in 2020. Main Measures: The two outcomes were clinical trial knowledge (no knowledge, a little knowledge, a lot of knowledge) and participation (never invited, invited did not participate, invited and participated). There were four binary indicators of health technology use for the following purposes in the past year: searching for health or medical information, communicating with a doctors office, looking up medical test results, and making medical appointments. There were four binary indicators of digital health engagement in the past year: sharing health information on social media, participating in a health forum or support group, watching health-related videos on YouTube, and awareness of ClinicalTrials.gov. Key Results: Survey-weighted multivariate regression models demonstrated that awareness of ClinicalTrials.gov had the largest associations with clinical trial knowledge and participation. Digital technology use to engage with medical providers and electronic health records was associated with clinical participation, although the vast majority of respondents had never been invited. Conclusions: Findings from this study can inform the design of large-scale digital health campaigns and quality improvement programs focused on increasing clinical trial participation.
背景：临床试验对科学进步和医学发展至关重要，但普通人群对临床试验的认知度和参与度仍然很低。现有文献表明，临床试验知识和参与是多因素的。然而，人们对临床试验的参与与健康技术的使用以及搜索健康信息、与医疗服务提供者互动和寻求健康支持的数字健康参与之间的关系知之甚少。研究目的研究临床试验知识和参与度与过去一年健康技术使用情况以及与医疗服务提供者的数字健康互动之间的多变量关联。设计：来自联邦监测系统的横断面数据。参试者2020年进行的第五次全国健康信息趋势调查（Health Information National Trends Survey 5）第4周期的3865名美国成人受访者。主要测量指标：两个结果是临床试验知识（不了解、略有了解、非常了解）和参与情况（从未受邀、受邀未参与、受邀并参与）。在过去一年中，健康技术的使用有四个二进制指标：搜索健康或医疗信息、与医生办公室沟通、查询医疗检查结果和预约就诊。在过去一年中，数字健康参与度有四个二进制指标：在社交媒体上分享健康信息、参加健康论坛或支持小组、在 YouTube 上观看健康相关视频以及了解 ClinicalTrials.gov。主要结果调查加权多元回归模型表明，ClinicalTrials.gov 的认知度与临床试验知识和参与度的关联度最大。使用数字技术与医疗服务提供者和电子健康记录互动与临床参与相关，尽管绝大多数受访者从未收到过邀请。结论：这项研究的结果可为设计大规模数字健康活动和质量改进计划提供参考，这些活动和计划的重点是提高临床试验的参与率。
{"title":"Effects of Health Technology Use and Digital Health Engagement on Clinical Trial Participation: Findings from the Health Information National Trends Survey","authors":"Nicholas C Peiper, Stephen Furmanek, Kelly C McCants, Edward Hoyt Brown","doi":"10.1101/2024.09.13.24312295","DOIUrl":"https://doi.org/10.1101/2024.09.13.24312295","url":null,"abstract":"Background: Clinical trials are critical to scientific advances and medical progress, although awareness and participation remain low in general populations. The existing literature indicates that clinical trial knowledge and participation is multifactorial. Yet, little is known about the association between clinical trial participation with health technology use and digital health engagement to search for health information, interact with medical providers, and seek health supports. Objective: Examine the multivariate association between clinical trial knowledge and participation with past-year health technology use and digital health engagement with medical providers. Design: Cross-sectional data from a federal surveillance system. Participants: A total of 3,865 US adult respondents from the Health Information National Trends Survey 5, Cycle 4 conducted in 2020. Main Measures: The two outcomes were clinical trial knowledge (no knowledge, a little knowledge, a lot of knowledge) and participation (never invited, invited did not participate, invited and participated). There were four binary indicators of health technology use for the following purposes in the past year: searching for health or medical information, communicating with a doctors office, looking up medical test results, and making medical appointments. There were four binary indicators of digital health engagement in the past year: sharing health information on social media, participating in a health forum or support group, watching health-related videos on YouTube, and awareness of ClinicalTrials.gov. Key Results: Survey-weighted multivariate regression models demonstrated that awareness of ClinicalTrials.gov had the largest associations with clinical trial knowledge and participation. Digital technology use to engage with medical providers and electronic health records was associated with clinical participation, although the vast majority of respondents had never been invited. Conclusions: Findings from this study can inform the design of large-scale digital health campaigns and quality improvement programs focused on increasing clinical trial participation.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-14DOI: 10.1101/2024.09.13.24313648
Stephanie Shiau, Xinhua Chen, Ayana April-Sanders, Ellen C Francis, Shristi Rawal, Megan Hansel, Kehinde Adeyemi, Zorimar Rivera-Nunez, Emily S Barrett
Background. Pregnancy is a unique stage of the life course characterized by trade-offs between the nutritional, immune, and metabolic needs of the mother and fetus. The Camden Study was originally initiated to examine nutritional status, growth, and birth outcomes in adolescent pregnancies and expanded to study dietary and molecular predictors of pregnancy complications and birth outcomes in young women. Methods. From 1985-2006, 4765 pregnant participants aged 12 years and older were recruited from Camden, NJ, one of the poorest cities in the U.S. The cohort reflects a population under-represented in perinatal cohort studies (45% Hispanic, 38% non-Hispanic Black, 17% White participants; 98% using Medicaid in pregnancy). Study visits, including questionnaires, dietary assessments, and biospecimen collection, occurred in early and late pregnancy as well as at delivery. Medical records were abstracted, and a subset of mothers and infants participated in a six-week postpartum visit. Results. Over the last five decades, the Camden Study has provided data toward the publication of numerous peer-reviewed papers. Results show that adolescent linear growth in pregnancy is associated with smaller birth size, possibly due to impaired hemodynamics. In the context of preterm birth and other perinatal outcomes, analyses of nutritional data have demonstrated the importance of micronutrients (e.g., folate, iron, zinc), as well as glucose/insulin dynamics and prenatal supplement use. More recent analyses have begun to unpack the biochemical pathways in pregnancy that may be shaped by race as an indicator for systemic racism. Conclusions. The Camden Study data and biorepositories are well-positioned to support future research aimed at better understanding perinatal health in under-represented women and infants. Linkages to subsequent health and administrative records and the potential for recontacting participants over 18-39 years after initial participation may provide key insights into the trajectories of maternal and child health across the life course.
{"title":"Cohort profile: The Camden Study - a pregnancy cohort study of pregnancy complications and birth outcomes in Camden, New Jersey, USA","authors":"Stephanie Shiau, Xinhua Chen, Ayana April-Sanders, Ellen C Francis, Shristi Rawal, Megan Hansel, Kehinde Adeyemi, Zorimar Rivera-Nunez, Emily S Barrett","doi":"10.1101/2024.09.13.24313648","DOIUrl":"https://doi.org/10.1101/2024.09.13.24313648","url":null,"abstract":"Background. Pregnancy is a unique stage of the life course characterized by trade-offs between the nutritional, immune, and metabolic needs of the mother and fetus. The Camden Study was originally initiated to examine nutritional status, growth, and birth outcomes in adolescent pregnancies and expanded to study dietary and molecular predictors of pregnancy complications and birth outcomes in young women. Methods. From 1985-2006, 4765 pregnant participants aged 12 years and older were recruited from Camden, NJ, one of the poorest cities in the U.S. The cohort reflects a population under-represented in perinatal cohort studies (45% Hispanic, 38% non-Hispanic Black, 17% White participants; 98% using Medicaid in pregnancy). Study visits, including questionnaires, dietary assessments, and biospecimen collection, occurred in early and late pregnancy as well as at delivery. Medical records were abstracted, and a subset of mothers and infants participated in a six-week postpartum visit. Results. Over the last five decades, the Camden Study has provided data toward the publication of numerous peer-reviewed papers. Results show that adolescent linear growth in pregnancy is associated with smaller birth size, possibly due to impaired hemodynamics. In the context of preterm birth and other perinatal outcomes, analyses of nutritional data have demonstrated the importance of micronutrients (e.g., folate, iron, zinc), as well as glucose/insulin dynamics and prenatal supplement use. More recent analyses have begun to unpack the biochemical pathways in pregnancy that may be shaped by race as an indicator for systemic racism. Conclusions. The Camden Study data and biorepositories are well-positioned to support future research aimed at better understanding perinatal health in under-represented women and infants. Linkages to subsequent health and administrative records and the potential for recontacting participants over 18-39 years after initial participation may provide key insights into the trajectories of maternal and child health across the life course.","PeriodicalId":501071,"journal":{"name":"medRxiv - Epidemiology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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