Odontogenic cysts (OC) are the most frequent lesions of the jaws and their constant epidemiological update is necessary and indispensable. Therefore the principal objective of this report was To determine prevalence and clinical-demographical characteristics of OC in a Mexican sample.p<0.05IC95%).753 cases of OC coming from the archive of a head and neck histopathological teaching service, from January 2000 to December 2013, were included. OC cases were re-assessed according 2005 WHO classification. Chi square test was used to establish possible associations (p 0.0002) at their fourth decade of life. Dentigerous cysts were more frequent in the mandibular posterior zone of males (p 0.0000) in their second decade of life. Six cases of periodontal lateral cyst; 4 cases of paradental cysts; 4 eruption cysts and 4 cases of adult gingival cyst, as well were identified.From 753 OC, 369 were female and 384 male; 52.9% of them were in their 2nd- 4th decade of life. The most common location (41%) was the mandibular posterior area. Radicular cysts were more frequent in maxillary anterior zone of females (Radicular cyst and dentigerous cyst are the most prevalent odontogenic cyst in this Mexican sample. Due to their etiology, dental pulpar necrosis and impacted teeth, radicular cyst and dentigerous cyst could be prevenible. Therefore, it is necessary to establish preventive strategies to diminish dental decay and programs of prophylactic extractions of impacted teeth, to in consequence decrease the prevalence of odontogenic cysts. Key words:Cyst, dentigerous cyst, mexican, odontogenic cyst, radicular cyst. Odontogenic cysts (OC) are pathological cavities, lined with odontogenic epithelium, which appear in both jaws and sporadically in the oral soft tissues principally the gums . OdontogIn 2005, World Health Organization (WHO) reclassified odontogenic keratocyst as a tumor and renamed it keratocystic odontogenic tumor, while calcifying odontogenic cyst was also classified as a tumor and was renamed Calcifying Cystic Odontogenic Tumor . In spitThe archive of Histopathological Diagnosis Service, at the Graduate and Research Division, Dental School, National Autonomous University of Mexico were reviewed in order to identify and select all cases with OC diagnosis. Time span covered was January 2000 - December 2013. The following demographical and clinical data were obtained from medical files: gender, age at moment of diagnosis, and lesion location. In this latter variable, both jaws (upper and lower) were divided into anterior zone and posterior zone. For research purposes, in the present article, anterior zone was considered from right upper canine to left upper canine in case of maxilla and from right lower canine to left lower canine in case of mandible; while posterior zone was considered the area comprised from the first bicuspid to the third molar, irrespective of whether it was right or left.In order to be included in the present study, cases should have sufficient biological material embedded in paraffin and/or histological slides stained with hematoxylin and eosin. In those cases which had enough biological material embedded but lacked histological slides, new cuts were obtained in order to be stained with the same staining technique. Two oral pathology experts (JPR/LAGC) reviewed all selected cases in order to confirm or refute diagnosis. Cases were grouped according to 2005-classification to Odontogenic Cyst and Tumors proposed by the WHO . Cases wThe present project adhered to research parameters established by the Mexican General Health Law, article 17, fraction I . This rep < 0.05, CI95%).An ex professo database was built with obtained data. Frequencies and relative frequencies of each OC type were obtained using the software program, SPSS® version 20 (IBM®). A chi square test was used to ascertain possible associations (p>0.05)  were found in females and 384 (50.9%) in males (p>0.05) . With reThe most prevalent anatomical location was the mandibular posterior area with 260 cases (34.5%), followed by the maxillary anterior zone with 193 cases (25.6%) and the maxillary posterior area with 144 cases (19.1%) .With respect to histological type, radicular cyst was the most common type with 408 cases, followed by dentigerous cyst with 309 cases, exhibiting a prevalence of 3.7% and 2.8% with respect to the total number of cases . Total and relative frequency of all diagnosed cysts is shown in p 0.0002). The most frequent location of radicular cyst was the maxillary anterior zone mainly associated to central and lateral incisor teeth. On the other hand, 309 cases of dentigerous cyst were reported, which represented 41% of all OC. Dentigerous cyst was more frequent in the mandibular posterior zone of males at their second decade of life, mainly associated to third molars. The association of dentigerous cyst and male gender was stastistically significant (p 0.0000). With respect to residual cysts, 18 cases were detected, most frequently found infemales at their 6th decade of life, located primarily in the posterior area of the mandible, mainly associated to premolar area.Radicular cysts represented 54.1% of all reported cysts and it was more frequent in females with an incidence peak at the fourth decade of life. Association of radicular cyst and female gender was statistically significant  is slightly higher than those reported in scientific literature, specifically when it is compared to those reports achieved using 1997 WHO classification, e.g.: 52.3% in the United Kingdom , 52.2% iWith respect to dentigerous cysts we observed the same pattern. We obtained one of the highest dentigerous cyst prevalence (41%), compared to data reported in studies using 1997 WHO classification, including reports on Mexicans conducted by Ledesma  in 2000 Four cases of paradental cyst were identified. The paradental cyst or inflammatory paradental cyst is defined as “a cyst occurring near to the cervical margin of the lateral aspect of a root as a consequence of an inflammatory process in the periodontal pocket; 61.4% of them are associated to partially or fully erupted 3rd vital mandibular molars ; 3 from On the other hand lateral periodontal cyst (LPC) is an uncommon development OC, representing about 0.4% of all OC. Lateral periodontal cysts develop in the alveolar bone along the lateral surface of a vital and erupted tooth. We obtainded a relative frequency for periodontal lateral cysts of 0.7%. LPC are most common in males into their 5th to 7th decades of life (mean age of 50.8 years) . HoweverTo our knowledge there are two previous reports about OC prevalence in Mexicans ,12, bothThe aforementioned aquires importance due to the fact that National Health Systems require accurate information regarding disease ocurrence in orden to effectively allocate resources and frame goverment health policies. Misleading or confusing data may inadvertently provide the wrong information to policy makers. Demographical data sustained by statistical data, are the basis for evidence-based policy. Accurate data will eventually enhance population health .OC are they most frequent intraosseous lesions of the jaws and therefore their demographical and clinical characteristics should be acknowledged by the general dentist. On the other hand, due to the fact of close relationship between dental caries and radicular cyst it is necesary to establish preventive strategies to diminish dental decay and in consequence decrease radicular cyst prevalence. This proposal becomes important since a preventive program not only impinges on their primary objective, but it additionally exerts a direct effect on bone lesions necessarily requiring surgical treatment which in some cases will elicit mutilations.


We find that gaps between siblings and neighbors born at different times of year are larger in more remote rural areas, farther from the equator where there are greater seasonal differences in rainfall and temperature. For those born at adverse times in places with pronounced seasonality, the gains associated with above-median proximity to nearby towns are similar to rising one quintile in the national distribution of household wealth for mortality, and two quintiles for attained height. Smoothing of outcomes could involve a variety of mechanisms to be addressed in future work, including access to food markets, health services, public assistance and temporary migration to achieve more uniform dietary intake, or less exposure and improved recovery from seasonal diseases.A large literature links early-life environmental shocks to later outcomes. This paper uses seasonal variation across the Democratic Republic of the Congo to test for  Smoothing nutrition goes beyond maintaining food security and diet quality to a deeper level of resilience, including prevention and recovery from disease [how people achieve stable nutritional outcomes, which is likely to depend on initial conditions and the type of shock to be absorbed. Many different mechanisms could be involved, such as agricultural production and food purchases to smooth dietary intake, health care services to prevent and treat seasonal diseases, or sanitation and vector control to block seasonal disease transmission. This study aims to define and measure nutrition smoothing using only cross-sectional data on the child’s birth month and nutrition outcomes, their location in terms of seasonal variation in rainfall and temperature, and their distance to towns or cities. Future work using other kinds of data would be needed to investigate which urban resources account for this smoothing effect, and how the more remote rural households might achieve the same degree of smoothing obtained by those closer to towns and cities.We define  disease . MeasuriThe outcomes of interest for this study are child mortality, height, and weight, which collectively are indicators of past nutrition and health conditions as well as predictors of future well-being. The setting is the Democratic Republic of Congo (henceforth DRC), one of the world’s poorest countries whose vast expanse generates a wide range of degrees of rural isolation, with differences in latitude north and south of the equator creating variation in the timing and severity of seasonal cycles. Since household location cannot be assigned experimentally, we use the randomness of a given child’s birth month to identify the causal effect of exposure to seasonal risk factors, in a spatial difference-in-differences approach. Spatial difference-in-differences has been used primarily in economic studies, for example to estimate the value of greening vacant urban land , or the In DRC and other settings, infrastructure and other investments to improve rural households’ access to markets and public services are well known to increase average productivity and welfare . As anotThe main contribution of this study is to define and measure the concept of nutrition smoothing. A secondary contribution is to demonstrate that this can be done using purely cross-sectional data, through a spatial difference-in-differences approach. In effect we treat each region as a repeated cross-section, using randomness in the timing of conception to identify the causal effect of exposure to seasonal rainfall and temperature, and various robustness checks on our tests for effect modification associated with proximity to town. DRC has many unique features influencing the study design, but the concept of nutrition smoothing and our method to measure it may have broad applicability in other settings.In DRC, approximately 75% of the population doesn’t consume sufficient calories for a healthy and active life , and theVariation in environmental conditions has opened countless opportunities to study the causes of differences in health and other child development outcomes –14. The Seasonal cycles in birth outcomes, child health, and farmer well-being has been observed around the world, including most recently in Brazil , and IndThe worst time to be born is an empirical question, and is likely to depend on the type of shock and the circumstances of the household. One of the few biological constraints is that total energy demands on the mother are typically greatest in the last trimester of pregnancy, at birth and while breastfeeding . This coThe aim of this study is to test whether proximity to towns and cities helps rural households avoid differences in health outcomes associated with seasonal fluctuations in rainfall and temperature. We build on the rich body of literature investigating the relationships between proximity to towns, price volatility, and consumption smoothing –43, partOur analytical method is spatial difference-in-differences, across three dimensions. First, we use climate data to identify regions with and without seasonal fluctuations. Next, we use the randomness of birth timing to identify exposure to seasonal risk where it exists. Finally, we use remoteness of households to identify whether proximity to towns confers resilience for children born in those places at riskier times. We use continuous variables for diagnostic regressions and exploratory exercises, then aggregate observations into dichotomous categories, add mother and community fixed effects and conduct various robustness checks against our identification strategy to address concerns about endogeneity and correlated errors .To address our research question, we merge spatial and temporal data on child health, household characteristics, roads, terrain, land cover, towns, and civil conflict incidents across DRC. The spatial units of observation are one degree by one degree grid cells, which avoids endogeneity problems that may arise with administratively-chosen boundaries . Each onOur main source of data on nutrition outcomes is the DRC’s nationally representative Demographic and Health Survey (DHS), which was conducted in 2007 and again in 2013. Data collection for the DHS complied with the Helsinki Declaration of 1983 on ethics in research on human subjects . The heiOur results control for exposure to civil conflict, which was and remains widespread in the study area. Conflict data are from the Armed Conflict Location and Event Dataset (ACLED), which details specific incidents of civil insecurity between 1997 and the present day for DRC and other countries .-1), enters as a measure of access to all kinds of markets and public services.We also incorporate geocoded data on 160 major towns from the Multipurpose Africover Database on Environmental Resources , calculaIn DHS data, the coordinates of each survey cluster have been randomly displaced by up to two kilometers in any direction for urban areas, and up to five kilometers for rural areas, with one percent of all survey clusters randomly displaced by up to 10 kilometers . This inThe naturally occurring random variation we exploit is the child’s month of birth, and hence their exposure to seasonal differences in rainfall and temperature. Our nutrition smoothing hypothesis is that potentially adverse conditions have smaller effects on child heights, weights and survival in locations that are closer to towns and cities. We control for time-invariant unobservable attributes of the child’s family and community using mother fixed effects in the mortality regressions, and cluster fixed effects in the height and weight regressions. We thereby compare each child to their siblings  and neighbors (for height and weight regressions) who were born at other times in the same place. To address the potential for spatially correlated errors, we pool children by risk exposure into dichotomous groups based on birth timing, degree of seasonal variation in rainfall, and distance to the nearest town. This dichotomous triple-difference approach to cross-sectional data mirrors standard difference-in-difference approaches with panel data, in which the identifying assumption of parallel trends is strengthened by comparing two pooled periods  and testOur analytical approach is illustrated in a priori “parallel trends” observation that seasonal variation in local rainfall and temperature is unrelated to the household’s proximity to town.As shown by the first two rows of rain months” to be the calendar month for households located in the southern hemisphere, and shifted 6 months forward for households in the northern hemisphere. For example, children born in the calendar month of January were recorded as such if in the southern hemisphere, and that month was recorded as “June” for the few children born in the northern hemisphere. These birth months were then aggregated into birth seasons, capturing a child’s exposure to similar seasonal conditions anywhere in the country using a single variable. With constructing this proxy measure of exposure to seasons, we were concerned with variations that are entirely predictable, and yet people may have been unable to avoid their negative impact. One reason could be that so many factors move together: during the hungry or lean season, food supplies from the previous harvests dwindle, gainful employment may be more difficult to find, disease incidence often increases, and maternal labor time and calorie expenditure may rise [To capture seasonality in the DRC context, we use the absolute value of latitude of each DHS cluster’s location. Locations closer to the equator have generally uniform temperature and rainfall throughout the year, while locations both north and south of the equator have a more pronounced dry “winter” season . The coumay rise –60.DHS data are typically collected in waves at specific times of year. The data we use were primarily collected in June 2007 and in December of 2013, as detailed in the supporting information. So, children born in earlier months (e.g. in May and in November of the respective survey round years) were surveyed at a younger age than those born in later months . Since height and weight z scores vary systematically with age, to avoid artifacts due to survey timing we follow  and contOur primary specification is an OLS regression with interaction terms to test for difference in differences, and mother or community fixed effects to absorb unobservable characteristics and compare siblings or neighbors born at different times of year. Standard errors are clustered at the community level, of which there are 300 in the 2007 survey and 540 in the 2013 survey, for a total of 840 locations.Zi on the left-hand sides of the equations: whether the child was alive at the time of the survey, the height-for-age z score (HAZ) and weight-for-height z score (WHZ). Each regression controls for age in months (Ai), or time elapsed since birth in the case of mortality regressions, in piecewise linear form as described above, and for child sex (Si) defined as 1 = male. Birth season (as BSi) enters as a binary variable (with 1 = births occurring between January through June in the southern hemisphere and occurring between July through December in the northern hemisphere). The absolute value of latitude for each DHS cluster j is used to stratify the sample between children around the equator who face little seasonal variation, and those farther from the equator who experience a dry winter season. Household wealth (Hi) enters as a categorical variable computed by DHS as quintiles of the national distribution, based on ownership of durable goods in the household. To control for civil conflict, we use a continuous measure (Cj) defined as the number of conflict fatalities recorded in the child’s grid-cell from their conception over their lifetime to the survey date. The underlying civil conflict data span from 2001 to 2013 for every grid cell in the country.There are three dependent variables of interest, indicated collectively by Rj) of whether the household is relatively remote, with 1 = household faces greater distance to travel to the nearest major town. The cut-off is 28.8km based on the median Euclidean distance in our sample. The Rj binary variable also enters in interaction with birth season to construct our difference-in-differences specification, where the estimated coefficients on that interaction (on the treated). A negative estimated ATE would indicate that being located far from town limits households’ ability to protect their children from harm.Household proximity to the nearest major town enters as a binary indicator . εi is a stochastic error term with the usual properties, and δfe refers to mother or location fixed effects.The reduced form econometric models are shown below in Eqs Latj) enters linearly and continuously as degrees, and household remoteness enters continuously as proximity to the nearest major town in km-1 (Pj). Descriptive statistics are presented in Exploratory t-tests for differences in child mortality, heights and weights across groups are shown in The onset and duration of stunting follow standard age patterns as shown in Variation in stunting and mortality by month of birth is shown in Figs To address the relationship among all our variables, Estimates from a pure difference-in-differences specification can be found in Results of our preferred difference-in-difference specification  are showThe average treatment effect of living farther from town (remoteness) when exposed to seasons is the estimated coefficient on the interaction term between them. Looking first at the “treatment” regions , the average treatment effect of household remoteness is statistically significant for survival and heights, but only in the locations with a dry winter season. The average treatment effect of remoteness is not significant for weights as an outcome. The effects for survival and heights are quite large in magnitude, with the height effect being similar to jumping about two quintiles in household wealth, and the survival effect similar to jumping one quintile in household wealth.To check the robustness of our results, we can look first within Since the DHS data were not collected uniformly over time, children born in different months were measured at different ages, and have consequently different levels of z score. Data for the DHS surveys utilized here were collected mainly during June for the 2007 survey and during December for the 2013 survey , or from seasonal patterns in miscarriage and neonatal mortality. The amplitude of the curve is slightly lessened when measuring by “We tested whether seasonality in birth month confounded our results by measuring the relationships between our binary season-of-birth variable against all the explanatory variables in our dataset. These results suggest that the potential effect of endogeneity of birth timing did not influencing our findings , one of the world’s most impoverished countries whose vast expanse straddles the equator, exposing households to differing degrees of seasonal rainfall variation, and whose sparse urbanization and lack of infrastructure gives households very different distances to the nearest towns. In this context, we use naturally occurring randomness in the timing of conception and birth to construct a spatial difference-in-differences test of whether households with easier access to markets and public services can use that to protect their children from seasonal fluctuations in malnutrition and disease. Future work with other kinds of data could address which urban resources confer resilience to which seasonal risks.An important feature of DRC is that children’s average health is not necessarily worst where seasonal variation is most extreme. For example, being geographically isolated could actually benefit households and children, as isolation and rugged terrain may protect them from violence or disease outbreaks in more populated areas . FurtherHealth outcomes in this study are child mortality, heights and weights at the time of the country’s 2007 and 2013 Demographic and Health Surveys (DHS). Our main result is that households’ proximity to towns and markets does protect children from seasonal fluctuations in health conditions at birth. For child height, the magnitude of gain from having above-median proximity to urban areas is similar in magnitude to the gain from being two quintiles higher in the local wealth distribution. Results of this magnitude are large but plausible, and help explain the rural-urban differences in health outcomes found in a wide variety of other settings. We subjected our findings to a variety of robustness tests, including comparisons of the estimated average treatment effect with similarly estimated coefficients in placebo regions and for placebo outcomes, selection bias in birth timing and child mortality, and other possible threats to identification.Further work such as  would beS1 FileTable A: Variance inflation factors (VIF). This table summarizes the variance inflation factors of key determinants of HAZ and WHZ in the final merged dataset. All variable definitions are as for Table B: Timing of data collection for 2007 survey. Notes: This table enumerates the timing of data collection for the 2007 Demographic and Health Survey for the Democratic Republic of the Congo by month. Table C: Timing of data collection for 2013 survey. Notes: This table enumerates the timing of data collection for the 2013 Demographic and Health Survey for the Democratic Republic of the Congo by month. Table D: Testing for endogeneity of birth timing, for whole sample and within climate zones. This table shows results of a robustness test which measures any endogeneity of birth timing in the data. The dependent variable is a binary indicator of birth during the Jan.-June wet season. The regression was estimated using fixed-effects logit. All results include fixed effects for survey clusters (N = 840), with notation and variable definitions as in Fig. A: Conflict incidents by month. Notes: This figure was generated using the ACLED [Fig. B: Mean age and HAZ at time of survey by calendar month of birth, 2007 DHS, Notes: This figure is Fig. C: Mean age and HAZ at time of survey by calendar month of birth, 2013 DHS. Notes: This figure is Fig. D: Timing of births by calendar month and season. Notes: Data shown are the number of children ever born in each month, as recorded across each DHS survey for DRC. The solid line refers to calendar months, and the dashed line uses a seasonal adjustment by hemisphere, where dates north of the equator are recorded as “January” for births in June, “February” for July, etc. In our regressions, these “rain months” are aggregated into six-month periods, since as children in higher latitudes who are born in the January-June period are more exposed to heavy rains and subsequently poor health outcomes than those born in the rest of the year. As shown here, more children were born in these adverse months than in July-December, as conception was slightly more likely to have occurred during the dry winter season. This pattern suggests that birth timing is either random or associated with factors other than variation in the child’s health prospects.he ACLED  for DRC.(DOCX)Click here for additional data file.


The global coverage rate of birth registration is only around 65% for the population of children under five although birth registration secures protection and access to health services that are fundamental rights for all babies. This study aimed to perform a basic analysis of the accessibility to birth registration to better understand how to improve the birth registration system in the Lao PDR.For the analysis of birth registration and related socioeconomic factors, 9576 mother-child pairs were chosen from the data set of The Lao Social Indicator Survey 2011–12. After bivariate analysis with statistical tests including the chi-square test were conducted, logistic regression was performed to determine the variables that statistically influence accessibility to birth registration.Ethno-geographic factors and place of delivery were observed to be the factors associated with birth registration in this analysis.Many mothers in the Lao PDR deliver in their local communities. Therefore, capacity development of various human resources, such as Skilled Birth Attendant, to support the local administrative procedure of birth registration in their communities could be one option to overcoming the bottlenecks in the birth registration process in the Lao PDR. Under the Convention of the Rights of the Child, all children have the right to have a legally registered name, to be officially recognized by the government and also to have a certified nationality . HoweverBasic demographic data collected by birth registration are crucial information for national planning and monitoring because they enable the creation of effective strategies not only for the health sector but for all sectors of development , 6. The According to the 2013 UNICEF report, the coverage rate of birth registration is only around 65% of the population of children under five worldwide . In NigeThe Lao PDR, the subject country of this analysis, has a rate of birth registration of 75% according to the 2015 UNICEF report . This fiTherefore, this study aimed to perform a basic analysis of the accessibility to birth registration to better understand methods to improve the birth registration system in the Lao PDR and to determine policy implications for overcoming the difficult achievement of birth registration in Lao PDR, where most mothers choose home delivery.We used data from the 2011–12 Lao Social Indicator Survey (LSIS) in the present analysis. The LSIS is a household-based survey that applies the technical frameworks of the Multiple Indicator Cluster Survey and the Demographic and Health Survey . LSIS waThe response rates for households, women and men were 99% , 94%  and 89% , respectively. Detailed information was obtained via interview on 11,067 (98%) of the 11,258 children under 5 years of age listed in the household questionnaire. Face-to-face interviews were conducted with all women aged 15–49 years and men aged 15–59 years in the sampled households by use of questionnaires covering socioeconomic, demographic and health indicators.The survey covers a broad range of topics including water and sanitation, marriage and sexual activity, fertility levels and trends, reproductive health, adult and maternal mortality, child health, nutrition, child mortality, child development, literacy and education, child protection, HIV/AIDS and sexual behavior, access to mass media and use of information/communication technology.Values related to the accessibility to birth registration across the categories of the explanatory variables are presented as numbers and percentages. The outcome indicator of birth registration includes children whose parents self-reported the possession of a birth certificate and children whose mother or caretaker said that the birth had been registered .From the LSIS data set, 9576 mother-child pairs for which complete data on socioeconomic stat was available were used for the analysis of birth registration and related socioeconomic factors.First, bivariate analysis was conducted to explore the proportion of children registered with respect to each socioeconomic factor. Statistical tests including the chi-square test were used to test whether there were significant associations between the responses about birth registration and the socioeconomic factors. The analysis used 14 socioeconomic factors at the child, maternal, household and community levels: child’s sex, child’s age in months, maternal age, marital status, maternal educational attainment, experience of delivery, experience of child loss, paternal educational attainment, head of household’s ethnicity, head of household’s ethno-language, head of household’s religion, wealth index quintiles, region and settlement. Bivariate analysis was also performed to clarify the relationships between place of delivery, delivery attendance and birth registration. Finally, we used logistic regression to explore the variables that statistically influenced accessibility to birth registration by preparing 3 logistic models  attendance”; and model 3 included “maternal, household, community variables, SBA attendance and place of delivery”). All analyses were done using IBM SPSS Statistics for Windows, Version 24.0.Figure Table In regard to community factors, there were broad-based variations in birth registration by region as shown in Fig. The results of accessibility to birth registration by delivery condition were significantly different Table . ChildreLogistic regression analysis was applied to determine the variables that statistically influenced the attainment of birth registration. Variables that were of no significance, variables related to the child and variables that had collinearity were excluded from the analysis beforehand. The results are presented in Table Maternal age was the significant factor for birth registration, rather than maternal educational attainment, in model 1. Higher educational attainment of the father and higher household wealth were likely to accelerate registration of a child’s birth. Khmer people were significantly less likely to have access to birth registration. SBA attendance was not associated with accessibility to birth registration. SBA attendance was excluded in model 2 and thus had no association with birth registration. In model 3, place of delivery, experience of child loss, religion and household index were not associated with birth registration in contrast to the results in models 1 and 2. As expected, children who were born in a community were less likely to have birth registration: 46% in health centers, 53% at home and 85% in other locations.In this analysis, we illustrated the geographical distribution of birth registration, and then we examined variables related to the outcomes of birth registration. Ethnicity of the head of the household, settlement and delivery place were observed to be the important factors related to accessibility of birth registration in this analysis.First, considerable geographical distribution was identified across all of the provinces. Accessibility in Huaphanh province and Pongsaly province in the Northern area was quite low compared with that in the Central and Southern areas. In the Lao PDR, the Northern area, in which ethnic minorities dwell, consists of a mountain range running along the border with China, Myanmar and Vietnam. In the multivariable analysis, settlement and ethnicity were the common factors that remained in all models, which means that ethno-geographic factors may determine the accessibility to birth registration. Even after adjustment for SBA attendance or place of delivery, settlement and ethnicity were still likely to be significant factors. Of great concern are the disparities between different regions in the nation, within regions and between urban and rural areas . The posBecause geographical disparity is a fundamental issue that cannot be avoided, we should further promote community-based intervention, which is in place everywhere in the Lao PDR. Considering the geographical disparities of accessibility to birth registration in many communities, community health volunteers (CHVs) assume a key role in issuing birth certificates and supervising the management of the family book in the Lao PDR. In some regions, CHVs work actively and have all of the information on vital statistics such as births or deaths –19. StreIn the multivariate analysis, not surprisingly, place of delivery was significantly associated with birth registration: the children who were born at a health center, at home or at other places were especially much less likely to have accessibility to birth registration. Only 65.0% of the children who were born at home had access to birth registration. This is 10 percentage points lower than the national representative average rate of birth registration of 75% . Widely To improve access to proper birth registration especially in pockets of the Lao PDR, one possible approach could be capacity building of SBAs in the community to support mothers’ access birth registration right after delivery . The WHOAlthough this study reached its aims and important policy implications can be drawn from the results, it does have an important limitation. The study was carefully prepared by conducting a statistical analysis using representative national data, but it is a secondary analysis using national data with limited available variables. Thus, conclusions about causality cannot be drawn, nor can all possible confounders be taken into account. Therefore, in terms of UHC, our analysis raises further questions about what are the other factors related to achieving birth registration in rural communities in the Lao PDR. Further investigation of birth registration is recommended to collect detailed community-based data that can contribute to improving accessibility to the birth registration system in the Lao PDR.Our findings confirmed that ethno-geographic factors and place of delivery appear to be the two most significant factors associated with accessibility to birth registration in the Lao PDR. As one effective strategy to overcoming geographical disparity in the Lao PDR, where most mothers choose home delivery, we recommend community-based interventions that focus on capacity development of community health personnel such as the SBA.


To quality assure a Trusted Third Party linked data set to prepare it for analysis.Birth registration and notification records from the Office for National Statistics for all births in England 2005–2014 linked to Maternity Hospital Episode Statistics (HES) delivery records by NHS Digital using mothers’ identifiers.All 6 676 912 births that occurred in England from 1 January 2005 to 31 December 2014.Every link between a registered birth and an HES delivery record for the study period was categorised as either the same baby or a different baby to the same mother, or as a wrong link, by comparing common baby data items and valid values in key fields with stepwise deterministic rules. Rates of preserved and discarded links were calculated and which features were more common in each group were assessed.Ninety-eight per cent of births originally linked to HES were left with one preserved link. The majority of discarded links were due to duplicate HES delivery records. Of the 4854 discarded links categorised as wrong links, clerical checks found 85% were false-positives links, 13% were quality assurance false negatives and 2% were undeterminable. Births linked using a less reliable stage of the linkage algorithm, births at home and in the London region, and with birth weight or gestational age values missing in HES were more likely to have all links discarded.Linkage error, data quality issues, and false negatives in the quality assurance procedure were uncovered. The procedure could be improved by allowing for transposition in date fields, and more discrimination between missing and differing values. The availability of identifiers in the datasets supported clerical checking. Other research using Trusted Third Party linkage should not assume the linked dataset is error-free or optimised for their analysis, and allow sufficient resources for this. The study is the first time linked birth registration and hospital delivery records for 10 years of data at the national level have been quality assured.A high linkage rate was maintained after quality assurance.The methodology was able to draw upon the availability of identifiers in both the Office for National Statistics (ONS) and Hospital Episode Statistics (HES) data to inform the choice of rules at each stage in the methodology, to carry out clerical checks to ensure the accuracy of the results and to explore data and linkage quality issues.Carrying the work out in the secure environment of ONS’ Virtual Microdata Laboratory safeguarded the data but limited software options and increased processing time.Any bias arising from non-submission of HES records by some trusts is likely to lead to bias in the extent to which records could be quality assured.The quality assurance procedure described here was developed to support a project3The routinely linked ONS birth registration and birth notification records from here on will be referred to collectively as ONS birth records.This paper describes quality assurance of the linked data for England, as data for Wales were linked separately.Quality assuring this linkage aims to achieve two goals simultaneously. The first is to ensure that the ONS birth record has linked to the correct HES delivery record. As HES can contain more than one episode record for a delivery, the second aim of quality assurance is to select the most relevant HES delivery episode record with the most information from all the available correctly linked records.The ONS birth records were for 6 468 586 singleton and 208 326 multiple births in England from 1 January 2005 to 31 December 2014.Only the Maternity HES delivery record which contains information on onset of labour and method of delivery was used. Maternity HES also contains a birth record for the baby, but linkage to these data and the further linkage to subsequent admissions of mothers and babies are not considered in this paper.HES delivery records are usually grouped by financial year and were provided for the financial years 1 April 2004 to 31 March 2015 in order to include the calendar years used by ONS for its publications and analyses. They are for finished consultant episodes which capture information about diagnoses and procedures during the time a patient spends under the care of an individual consultant, or in the case of maternity, a midwife. Each delivery record contains an admitted patient care record with additional fields about the mother in an appended maternity tail, and information on the baby in an appended baby tail for up to nine babies.Singleton and multiple birth deliveries are not explicitly identified in HES, but multiple delivery records can be identified where the diagnostic codes and the number of babies indicate a multiple birth, and vice versa for singletons. On this basis, 7 040 590 HES delivery records relating to singleton births, and 123 497 relating to multiple births were identified. As stated above, the singleton and multiple births were quality assured separately. There can be more than one episode record per delivery, and some records are duplicated, therefore the number of HES delivery records is higher than the number of births.Full details of the ONS and HES data are given elsewhere10–13Linkage of the ONS birth records to the Maternity HES delivery records was carried out by the Health and Social Care Information Centre, now known as NHS Digital, at the person level, using maternal identifiers because HES delivery records are associated with mothers rather than their babies. It consists of a hierarchical stepwise linkage based on combinations of NHS number, date of birth, sex and postcode of the mother’s place of residence see . Every lMothers’ identifiers from ONS birth records were linked first of all to the HES Patient Index to assign a HESID to each. The HESID is a pseudonymised number which uniquely identifies a patient and provides a way of tracking them in HES.Instances where the linkage algorithm did not link a registered birth to an HES delivery record could be because no delivery record existed, for example, for home births and births in private health facilities which are not usually captured by HES, non-submission of HES data by particular trusts at particular times, or due to linkage error. Overall, 1.7% of singleton and 4.3% of multiple ONS birth records without a link to an HES delivery record did have a link to the mother’s HESID. These problems have not been investigated further in this paper.This paper focuses only on validating the records that were linked to a mother’s HESID and HES delivery record by NHS Digital’s algorithm. Singleton and multiple births were assessed separately due to the extra complexity associated with multiple birth registration records. This paper sets out the quality assurance procedures and results for both.The aim was to assess the linked ONS birth and HES delivery records at the national level to efficiently identify one correctly linked HES record with the maximum amount of delivery information from all possible links to create a file to be used in analysis.Both mothers and babies’ identifiers were available for the ONS birth and HES delivery records to use in the quality assurance process. These were mother’s date of birth, NHS number and postcode of usual residence, and baby date of birth, NHS number and sex.The work was carried out in the secure environment of ONS’ Virtual Microdata Laboratory (VML). Microsoft Access database software was used as it was the only database software available in the VML at that time, and Structured Query Language (SQL) code was used for replicability. The relatively large size of the data files and restrictions on database size and processing speed constrained the design of the methods used.The REporting of studies Conducted using Observational Routinely-collected Data (RECORD)It was necessary to normalise the baby information in the maternity tail section of each HES delivery record so that the data could be stored and interrogated efficiently in the database environment. This meant that if information was held on more than one baby in the maternity tail part of a delivery record, this was transformed into separate records for each baby. For example, this meant two records, one for each of the two babies in a twin delivery. Each record contained the same common episode identifier, admitted patient care fields and maternity tail fields, but with the respective baby’s information in each one.For variables common to birth registration and birth notification, the maximum amount of reliable information was derived from the two sources. The baby’s date of birth, birth weight and sex were taken from birth registration, and gestational age was taken from birth notification, unless values were missing.Data types and formats, for example, date fields and values for the baby’s sex, were made consistent between ONS and HES records.The extent of missing values for key items of data about the baby in the ONS birth records for 1 January 2005 to 31 December 2014 and HES normalised records for 1 April 2004 to 31 March 2015 is given in The HES delivery data have a much higher proportion of records with missing values than ONS birth records, for example, up to 37% for gestational age in singleton births. The proportion of records with missing information is lower for multiple births in HES, as data items for the first baby are often repeated for their siblings in the same delivery.Where the baby’s date of birth was missing on HES, it was derived from operation dates if a delivery code was present. If sex of baby, gestational age or birth weight was missing, they could not be derived, however.Gestational age and birth weight are known to have problems, notably implausible values, truncation and rounding in HES. ONS birth records can also have implausible values for birth weight but this has been improving with the introduction of ‘warning’ edits to birth notification systems.There are few examples of existing procedures to quality assure linked data sets. One exampleA pilot was initially carried out on data for 2005 to help formulate the methodology. The following procedure is the final version that was carried out on all 10 years of the linked data. Clerical checks were carried out throughout the development phase using relevant variables and identifiers to inform and improve the procedure.On the assumption that the maternal linkage was correct, the links between the ONS birth records and HES delivery records were categorised as either ‘same mother same baby (SMSB) or ‘same mother different baby’ (SMDB). The category ‘wrong link’ (WL) was for where neither of the previous categories apply, suggesting incorrect maternal linkage or data quality problems. These scenarios are summarised in An ONS birth record may link to one correct or incorrect HES delivery record, or to more than one HES delivery record with one, none or some of them being correct for any of the reasons in The quality assurance exercise aimed to keep ‘same mother same baby’ linked records , and move all others to the unlinked file. If more than one correct link was available, only one linked delivery record containing the maximum delivery information was chosen. This was to create a one-to-one relationship and support ease of analysis.Baby data items were compared to ascertain if the linked records related to the same baby. These were baby’s date of birth (derived date of birth in HES where applicable), sex, gestational age, birth weight and location of birth.The place of birth codes used differed between the data sets. These were translated into a consistent common location code, designed specifically for the study.Locations of births out-with an NHS trust, such as home births, were all treated as potential location matches because they could ultimately be transferred to an NHS hospital and have an HES delivery record, and the recording of the location for such births in HES can be unreliable. For these cases, the other baby data items were relied on to establish if records were for the same baby.Four combinations of these variables were used to confirm whether or not a linked ONS birth record and HES delivery record related to the same baby see , in ordeIf the linked records referred to the same baby and there was more than one linked HES delivery record to choose from, then the record with the maximum onset of delivery and delivery method information was chosen, as these variables were of key importance for the analysis. The presence of valid values in these and other fields was used to inform this.Box 1Exclude links to invalid HES delivery records.Exclude links to surplus × 6 or x 9 duplicate HES delivery records.Exclude links to HES delivery records where the HES baby date of birth is too far away from the Office for National Statistics (ONS) baby date of birth to be the same birth.Keep if baby data items match.Exclude if baby data items do not match.Keep episode if baby data items match and contain maximum delivery information.Exclude episode(s) if baby data items do not match and/or episode contains less delivery information.Keep record if baby data items match with latest episode dates.Exclude record(s) if baby data items do not match and/or earlier episode dates.Keep record if baby data items match and contain maximum delivery information.Exclude record(s) if baby data items do not match and/or contain less delivery information.The procedure was implemented using deterministic stepwise rules to be carried out in order, with any linked records dealt with at each stage excluded from subsequent stages. A summary is given in 10.1136/bmjopen-2017-017898.supp1Supplementary file 1The first two rules cleaned the HES data by eliminating invalid linked records and duplicates. It is known that some trusts generate multiple copies of the same delivery episode record, usually six or nine copies of the same record. In these cases, only one was kept to be quality assured and the others were discarded for efficiency.Once all rules in the procedure had been carried out, it was confirmed that all linkages had been assigned a decision, and there were no more records to check.Private hospital births should not have a link to an HES delivery record unless the mother or baby was later transferred to an NHS hospital for any reason.Each HES delivery record should link to the correct number of ONS birth records depending if it is for a singleton or multiple delivery.The ONS birth record baby date of birth fell within the HES delivery admission period (no admission dates for home births). This was not always the case even when it was a confident link.If there was more than one correct and relevant linked HES delivery record for an ONS birth record, any missing onset of delivery and method of delivery information was filled in from these.Further checks were made on:The quality assurance procedure took the equivalent of 55 working days for singleton births and 16 days for multiple births. The database software and processing limitations of the VML at that time slowed the procedure down. SQL Server has since been made available in VML and would speed this up considerably.Seventeen per cent of singleton birth linked records were dealt with by the first two cleaning rules, and a further 80% were dealt with by rules 3 and 4. All the remaining rules were required to deal with the residual 3% of more complex cases.Five per cent of multiple birth linked records were dealt with by the first two cleaning rules, and a further 60% were dealt with by rules 3 and 4. This left 35% as residual records, reflecting the greater complexity in multiple birth records.For each year, the complex residual cases were clustered within particular trusts. This was probably due to information technology problems and changes to systems.A summary of the results for singleton and multiple births is given in Of the total 6 468 586 singleton ONS birth records, 97% had been linked to one or more HES delivery records and could be quality assured. Of these, 98% remained linked to one HES delivery record and 2% had all HES links discarded after quality assurance. Ninety-five per cent of all ONS singleton birth records overall were left with a link to an HES delivery record for analysis.Of the 208 326 multiple births, 95% were linked to one or more HES delivery records and could be quality assured. Of these, 98% remained linked to one HES delivery record and 2% had all HES links discarded after quality assurance. Ninety-three per cent of all ONS multiple birth records overall were left with a link to an HES delivery record for analysis.For singleton births, the percentage of all birth records left linked to one HES delivery record after quality assurance increased more or less year on year from 93% in 2005 to 95% in 2014. For multiple births, the rate increased from 94% in 2005 to 97% in 2010, but then started to decrease again to 87% in 2014. This reflects the same patterns as the percentage of ONS birth records that were originally linked to an HES delivery record and are biased by the number of records that linked to a HESID but no delivery records that were excluded from quality assurance.After quality assurance, all or some of the available linked HES delivery records will have been discarded, leaving only a link to one correct HES record, or to none at all. Discarding all the linked HES records for a birth suggests incorrect linkage and is discussed in the next section. Discarding all but one is due to a variety of reasons why there may be more than one HES delivery record available for a birth.Seventy-six per cent of all the linked HES delivery records that were discarded were due to duplicate copies of HES delivery episodes, and 0.5% were due to invalid delivery records, dealt with in the cleaning stages 1 and 2 of the methodology. A summary of the remaining reasons by year for singleton births is given in ‘Same mother different baby’ (column A) accounted for 60% of all discarded links. This is expected when the study period covers 10 years and each birth is linked by the mothers’ identifiers to all other deliveries to that mother in that period. The second main reason which accounted for 21% of discarded links was when there were additional episodes relating to a delivery, usually a predelivery or postdelivery admission (column B). The numbers of these generally decreased over time, suggesting data quality is a factor. The third main reason, accounting for 16%, was when links to other episodes in a spell have been discarded, keeping only the link to the episode with the most amount of delivery information (column C).The remaining three reasons each accounted for fewer than 2% of discarded links. Column D is when multiple delivery records contained the same information and links to superfluous records were discarded. This is also a data quality issue. There was a large increase in these in 2014. Column F relates to a small number of linked singleton birth records that were identified as ‘potential multiple births’, described later in this paper.Column E is ‘wrong links’; links discarded where there are no matching variables or insufficient numbers of matching variables according to the quality assurance methodology, and the baby’s date of birth did not indicate a ‘same mother different baby’. This group is of interest because it could be indicating erroneous false-positive links by the linkage algorithm, or it could indicate erroneous false negatives in the quality assurance methodology.Fifty-seven per cent (2743) of this group was linked by match rank 1 of the linkage algorithm, the most reliable stage of the linkage algorithm that matches all available identifiers including the NHS number, suggesting they should be correct maternal linkages. These were checked in greater detail clerically for explanations.Of these, 2323 (85%) were true wrong links where either none of the five baby data items matched at all, or only the place of birth matched, or only sex matched, or only gestation matched. The baby’s date of birth had not been transposed nor was ‘same mother different baby’, suggesting incorrect maternal linkage or some other data quality issue. This is 0.04% of all singleton birth records originally linked to an HES delivery record.It appeared that 366 (13%) were potential false negatives made in error by the quality assurance methodology. The dates of birth in these do not match due to transposition in the date of birth elements. If this had been factored into the methodology, these links would have not been discarded.The remaining 94 (3%) were undeterminable, with some discriminatory baby data items matching and some not. For example, the date of birth may match or have been transposed, but birth weight and/or place of birth may be very different.Some of the categories of problem shown in Checks were carried out to ascertain if the ONS birth records that remained correctly linked to one HES delivery record after the quality assurance procedure, and the ONS birth records with all links to HES delivery records discarded after the quality assurance procedure differed significantly by key variables. Discarding all links could indicate linkage error, data quality issues or false negatives in the quality assurance methodology.2 test results for testing the null hypothesis that the distribution of a variable is the same in the two groups. Variables from the ONS birth record include the match rank of the linkage algorithm, the region of birth, the year of birth, the month of birth, the day of birth (8) based on an 8-day categorisation including public holidays, the day of birth (11) based on an 11-day categorisation including specific public holidays and the days before and after them, the hour of birth, the age of the mother, the sex of the baby, the ethnicity of the baby, gestational age, if a stillbirth, and the location of birth. Missing gestational age and missing birth weight in HES were also included.Online 2 test can pick up subtle differences, but this does not tell us very much. Comparing the difference in the percentage distribution of values for each variable for the two groups provides more information on how they differ. This is given in The null hypothesis is rejected for all variables except for sex, day (8) and day (11) in multiple births. Finding significant differences is to be expected because the data set is large and the ΧThese differences give an indication of when incorrect linkage is more likely to occur, or indeed false negatives in the quality assurance. Differences between singleton and multiple births are seen. Some of these associations with incorrect linkage make sense. Home births are less likely to have an HES delivery record as many are not captured by hospital-based clinical systems from which HES delivery records are derived. Match rank 6 is a less reliable stage of the linkage algorithm because it does not match on the NHS number. HES data quality is previously known to be poorer in years 2006 and 2007, although for multiple births, years 2012–2014 are more likely to be incorrectly linked. Missing data for gestational age and birth weight in HES values means there is less information to make a correct link. The other factors require more investigation or could be simply due to chance with the large numbers involved.Online Quality assurance of the linked data set for such a large population revealed issues with the quality of the data within each of the data sets and also the quality of linkage that otherwise might not be detectable. While it is already known that HES has data quality issues, issues were found in the ONS birth records that warranted further investigation.First, the consistent common location code that was assigned to the place of birth codes was found to not match between birth registration and notification for 22% of the singleton births with a correct HES delivery link after quality assurance. This suggested the linkage between birth registration and notification could be incorrect. A sample of these records that also had very discordant mother and baby identifiers and baby tail information was given to ONS, which carried out this linkage, for checking.ONS confirmed that the baby NHS number or the sequence numberSecond, a small number of singleton birth records appeared to be multiple birth records see  column FLastly, after quality assurance, a small number of multiple births did not have all the expected number of ONS birth records, for example, a twin birth only had one ONS birth record associated with it rather than two. These were not always explained by stillbirths prior to 24 weeks’ gestation which should not be registered as a birth. Investigation revealed that the missing ONS birth records could often be found in the ‘unlinked’ ONS birth records file, to which no HES records were linked. This could be the case even if the mothers’ identifiers, including NHS number, clearly matched.This was discussed with our contact at NHS Digital who investigated this. It was explained that these missed links were due either to the HES Patient Index having been updated since the linkage was done originally, or linkage error. The project did not attempt to find any further missing links.The advantage of having the identifiers available to the project team for both ONS birth and HES delivery records made it possible to investigate and explain the above issues, but other research projects may not. Overall, this highlights how researchers and analysts should not assume that any routinely collected administrative data sets and linkages carried out between them are error free.This paper describes the quality assurance process that was carried out on 10 years of linked ONS birth records and Maternity HES delivery records at a national level using patient identifiers. The aim was to prepare the linked data set for analysis but the results also shed light on the quality of the data and the linkage itself. This makes it informative for other users of similarly linked data and for NHS Digital staff doing the linkage.The method adopted compares common baby data items, giving most importance to matching babies’ date of birth and location of birth. Altogether, 95% of all singleton births and 93% of all multiple births in England in the period 2005–2014 were linked to one correct HES delivery record after quality assurance.Administrative duplicate HES records were found to be the main reason for discarding links. Other reasons were due to other births to the same mother within the study period, the presence of multiple HES records per delivery for various reasons, and error in the linkage and in the quality assurance method itself.Certain features were more common in incorrectly linked records than correctly linked records. These included births linked using match rank 6 of the linkage algorithm, births at home and in the London region, and births where the birth weight or gestational age was missing in HES. This suggests some bias in the linkage and often relates to data quality issues. These patterns differed between singleton and multiple births.Missed links were also discovered, which together with the previous findings provide further evidence on the quality of the linkage.It was possible to identify aspects of the quality of the ONS birth records data and have ONS investigate them.The quality assurance procedure was able to draw upon the availability of identifiers in both the ONS and HES data to inform the choice of rules at each stage in the methodology, to carry out clerical checks to ensure the accuracy of the results, and to explore data and linkage quality issues. The procedure successfully identified correct and incorrect linkages to prepare the large linked data set for analysis within the resources available.Because personally identifiable data were used, the work was carried out in the secure environment of ONS’ VML. While security was safeguarded, this led to many delays and disruption due to software and processing limitations of systems and servers. This was costly in terms of researcher time and has limited the extent of analytical outputs from the study.Quality assurance could only be carried out on ONS birth records that had linked to an HES delivery record. Links where a HESID was found but no delivery record was available were excluded. If the main reason for this is because some trusts did not submit the HES delivery record, then bias will be inherently present in which linked records could be quality assured and which could not.The methodology to decide if the linked records related to the same baby was purely deterministic and did not allow for transposition of date of birth elements, or for rounding and truncating in the birth weight and gestational age values. It did not strongly discriminate between when values were missing rather than different.A disproportionate amount of effort was spent on the approximately 4% of complex linked records that remained after stages 1–4 of the methodology.Future work could address these issues by incorporating more flexible matching criteria and more thoroughly preparing the birth weight and gestational age variables for data entry errors beforehand. Ways to simplify the methodology to fewer variable combinations or rules to identify correct and incorrect links could be explored. Future work could also assess how variation in data quality at trust level affects linkage quality and the results.The linkage error identified by quality assuring the linked data set suggests that linkage carried out using similar algorithms and by ‘Trusted Third Parties’ generally, cannot be assumed to be error free and may affect any analyses carried out on them. This has implications for other research projects that are not informed of the quality of the linkage, or have the means to quality assure it themselves using identifiers. In particular, no assessment of the quality of the linkage algorithm is routinely published by NHS Digital.A very similar linkage algorithm is used to routinely link HES data to ONS death registration data, differing only at match rank 8,Linkage of identifiable data for research purposes is mostly carried out by ‘Trusted Third Parties’ such as NHS Digital. The Trusted Third Party model replaces identifiers with an encrypted code, and only the de-identified data are made available to researchers in a secure environment. The Digital Economy BillIn the context of the use of linked data for statistics, the ONS has expressed a preference to be able to link data with identifiers rather than encryption to achieve better quality official population statistics in the future.26Carrying out quality assurance of linked records has informed knowledge of both the quality of the data and of the linkage, providing benefits over cleaning each data set in isolation. Other research projects using large linked routinely collected data sets should not assume the linked data sets are error free or optimised for their analysis. This has implications especially for users of anonymised linked data. The resources needed to do quality assurance and prepare effectively prior to generating descriptive or analytic reports are considerable and need to be addressed when planning and costing large data linkage projects.


We further assessed the association of these changes with CKD in an independent cohort, and with a previously established urinary proteomic panel, termed CKD273. 107 individual peptides significantly changed after treatment. The majority of the empagliflozin-induced changes were in the direction of “CKD absent” when compare to patients with CKD and controls. A classifier consisting of these 107 peptides scored significantly different in controls, in comparison to CKD patients. However, empagliflozin did not impact the CKD273 classifier. Our data indicate that empagliflozin induces multiple significant changes in the urinary proteomic markers such as mucin and clusterin. The relationship between empagliflozin-induced proteomic changes and clinical outcomes merits further investigation.Treatment with empagliflozin, an inhibitor of the sodium/glucose cotransporter 2 (SGLT2), is associated with slower progression of diabetic kidney disease. In this analysis, we explored the hypothesis that empagliflozin may have an impact on urinary peptides associated with chronic kidney disease (CKD). In this  Diabetes-associated vascular diseases, especially chronic kidney disease (CKD) represent a major burden for developed societies . Today, Multiple recent reports have demonstrated the potential of proteomic biomarkers in kidney disease, as reviewed in . While ipost-hoc, exploratory analysis was to investigate if treatment with empagliflozin impacts the urinary peptidome, with a special emphasis on urinary peptide biomarkers associated with CKD–specifically the CKD273 biomarker, which is associated with CKD progression [The aim of this gression . Such anSamples were collected as described in previous work during clamped euglycemia and hyperglycemia in uncomplicated patients with T1D before and after treatment with empagliflozin (25 mg daily) for 8 weeks . All patThe urine samples were prepared and analyzed using a P/ACE MDQ capillary electrophoresis system  on line coupled to a MicroTOF MS  described previously in detail . AccuracTo generate a peptide pattern indicative of the impact of empogliflazin, the support vector machine (SVM)-based MosaCluster software  was emplAfter testing for normal distribution, continuous data were compared by Wilcoxon rank-sum test, as this test has proven to be of superior statistical power in proteomics datasets . A p-valS1 Table. To increase statistical power, data from baseline samples collected during clamped euglycemia and hyperglycemia were combined for the analysis, and the same was done for all follow-up samples obtained after empagliflozin treatment for 8 weeks. The investigation of the proteome of these 160 samples enabled identification of 107 peptides that changed significantly between the baseline and post-treatment samples (S2 Table). Fig 1 illustrates the distribution of the 107 peptides before and after treatment with empagliflozin.All available samples were analyzed blinded. All data on the individual samples are available in To obtain information on the potential relevance of these changes in the context of CKD, the distribution of these peptides in the cohort of patients with CKD and controls that was used to define CKD273 was investigated. This cohort consisted of 379 healthy controls, and 230 patients with CKD of various etiologies (including 50 patients with diabetic nephropathy) . Of the Table 1. When investigating these 46 sequenced peptides only, the significant changes in peptides derived from clusterin, alpha-1-antitrypsin, keratin type 2, and mucin were in the direction towards “healthy”, when comparing healthy controls and CKD. The results were less consistent when investigating the change in collagen fragments induced by empagliflozin: larger collagen fragments generally appeared to be increased upon treatment, while smaller fragments appear to be decreased, possibly indicating reduction of specific protease activity involved in the processing of collagen.Amino acid sequences were obtained for 46 of the 79 peptides, all listed in Fig 2). These data further support the initial findings, that the changes induced with empagliflozin are directional pointing away from CKD and towards the healthy controls , indicating a potential benefit of empagliflozin.To investigate how the changes in these 107 peptides relate to CKD, we combined all 107 peptides into an SVM-based classifier to receive a composite score, using the data obtained from all samples investigated in the context of empagliflozin treatment. The classifier was trained using all 160 samples, and allowed clear differentiation between the two groups (treated and untreated) in the training set. This classifier was subsequently applied onto the 609 samples that were also employed in the discovery of CKD273 in the past  to invesFig 3, levels of CKD273 increased within the negative (CKD absent) range during clamped euglycemia, but not during clamped hyperglycemia. Overall, no consistent impact of the empagliflozin treatment on CKD273 could be observed in this cohort.To further assess the effect of empagliflozin, we applied CKD273, a high-dimensional classifier based on 273 urinary peptides that were found significantly changed in CKD ,22, ontoIn this analysis we investigated the impact of empagliflozin treatment on the urine proteome in type I diabetes patients with preserved kidney function. In several recent studies urinary proteomic changes were reported as a consequence of therapeutic intervention, both based on drugs ,23, but Of special interest is the upregulation of a mucin fragment as a result of empagliflozin treatment. In a recent manuscript we have identified the decrease of mucin fragments in urine as a major component in the CKD-induced changes . The datEmpagliflozin is the first compound that to our knowledge demonstrates a rapid and significant impact on urinary peptides in the context of diabetes and CKD. In our previous investigations, while short term treatment with irbesartan  did not We also investigated the impact of empagliflozin treatment on CKD273, a urinary peptide-based classifier associated with CKD onset and progression ,11,17. Apost-hoc study has limitations: The sample size was small and the treatment period was only 8 weeks, perhaps not sufficiently long to observe significant changes in CKD273. The results may not be generalizable to patients with either type 2 diabetes or patients with evidence of renal disease. Whether SGLT2 inhibition impacts on CKD273 in patients with a background of renal disease is unknown, and will be examined in future work.This exploratory, In conclusion, empagliflozin has a significant impact on specific urine peptides, and the proteomic changes induced by empagliflozin suggest a potential nephroprotective effect. However, this potential nephroprotective effect needs to be investigated and confirmed in dedicated clinical trials that are designed to assess the impact of empagliflozin in patients with CKD, and investigate how these proteomic changes correlate with clinical outcomes.S1 TableOriginal ID of the samples is given on the top. For all peptides or proteins, internal Peptide ID, mass and migration is given. In addition, sequence, original protein, as well as start and stop amino acid position is given, where applicable.(XLSX)Click here for additional data file.S2 TableThe table lists the mass (in Da) and migration time , the relative abundance before (Pre EMPA) and after (Post EMPA) treatment, and the fold-change induced by empagliflozin (EMPA-ind change). Further, the average abundance in patients with CKD (CKD mean) and controls (control mean) as well as the fold-change observed in CKD (CKD-induced) is listed. Where applicable, the Sequence, and protein Name and Symbol are given. For all peptides, the unadjusted p-value (for the difference prior and after empagliflozin treatment), the AUC, and the Benjamini-Hochberg (BH) adjusted p-value is listed.(XLSX)Click here for additional data file.


Maternity Hospital Episode Statistics (HES) data for 2005–2014 were linked to birth registration and birth notification data (previously known as NHS Numbers for Babies or NN4B) to bring together some key demographic and clinical data items not otherwise available at a national level. The linkage algorithm that was previously used to link 2005–2007 data was revised to improve the linkage rate and reduce the number of duplicate HES records.Birth registration and notification linked records from the Office for National Statistics (‘ONS birth records’) were further linked to Maternity HES delivery and birth records using the NHS Number and other direct identifiers if the NHS Number was missing.For the period 2005–2014, over 94% of birth registration and notification records were correctly linked to HES delivery records. Two per cent of the ONS birth records were incorrectly linked to the HES delivery record and 5% of ONS birth records were linked to more than one HES delivery record. Therefore, a considerable amount of time was spent in quality assuring these files.The linkage rate for birth registration and notification records to HES delivery records steadily improved from 2005 to 2014 due to improvement in the quality and completeness of patient identifiers in both HES and birth notification data. Linking three national data on births together greatly increased the number of variables available for analysis.The findings are relevant for other users of trusted third party linkage who should not assume that datasets linked using patient identifiers are error free and may affect any analyses carried out on them.Data are held in a secure environment at the Office for National Statistics so access is restricted but can be used by approved researchers.When a baby is born in England and Wales, data are recorded in several separate information systems, namely birth registrations where mainly socio-demographic data are collected. A smaller set of data is recorded when the birth is notified to the NHS and the NHS Number, a unique identifier, is issued. Data about care at delivery are recorded in the Hospital Episode Statistics (HES) if the birth occurs in England. Data about care at delivery in Wales are recorded in the Patient Episode Database for Wales which is linked to the National Community Child Health database. Each of these systems includes common data items such as the baby’s and mother’s date of birth, postcode of residence and NHS Number which can be used as identifiers for record linkage.In England and Wales, all live births must be registered within 42 days. The data recorded at registration include names, address of residence, place of birth, occupation of the parents and country of birth of mother and the father.2–4A collaborative project was set up in 2004 between City University London, the Office for National Statistics (ONS) and the Welsh Assembly to link these datasets for all births that occurred in England and Wales from 2005 to 2007. Stage 1 of the project involved linkage of birth registration data with the birth notification data (previously known as NN4B dataset) and assessment of the quality and completeness of the notification data. This was piloted on the 2005 data.7Stage 2 of the project involved linkage of the dataset created in stage 1 to Maternity HES and assessment of data quality and completeness by comparison with birth registration or notification dataset, where possible.9The primary focus of the first two projects was to test the feasibility of the linkages and assess the quality of the linked datasets. The next project aimed to answer a specific set of research questions. In 2013, a project was funded to describe and analyse daily, weekly and yearly cyclical variations in births and their outcome and explore the potential implications of the patterns observed for NHS staffing and for service users. This involved extension of the linkage in stages 1 and 2 to include births occurring in England and Wales between 1 January 2005 and 31 December 2014. This article describes the linkage of data for England. As before, data for Wales were linked separately.Several variables are common to all three data sources, Maternity HES, birth registration and birth notification, as can be seen in The source data, birth registration, birth notification and Maternity HES are described in detail in the article describing the linkage of data for 2005 and 2006.8There are two types of maternity records in HES: the delivery record and the birth record. Both types of records consist of an admitted patient care record with an additional 19 fields, in an appended baby ‘tail’.The HES delivery record is a mother-based record containing the mother’s details with a maternity tail and a baby tail which can accommodate up to nine babies born in one maternity. In contrast, the birth registration and notification linked data consist of one record per baby. Therefore, the linkage was based on linking babies to their mothers' records.A HES birth record is generated for the baby. It contains the baby’s details and also has a baby tail containing the same type of information that is recorded in the corresponding baby tail of the mother’s delivery record.trusts submitting a significantly higher number of delivery episodes compared with birth episodestrusts failing to submit data on the number of birth episodes where they record a higher number of delivery episodes.The baby tail data coverage is less complete than the rest of the HES data. There are a number of reasons for the incompleteness and data quality issues, such asPatient identifiers including mother’s and baby’s NHS numbers, postcode of residence, mother’s and baby’s date of birth and baby’s sex together with a unique record ID were extracted by ONS from the linked birth registration and notification file and sent to the data linkage team at the Health and Social Care Information Centre, now known as NHS Digital.The linkage algorithm that had been previously used to link 2005–2007 births was used to link further years data 2008–2014. ONS identifiers were first linked to the HES index to obtain HES patient identifiers known as HESIDs.10.1136/bmjopen-2017-017897.supp1Supplementary file 1The linked data provided by NHS Digital consisted of two files for each financial year from 2004/2005 to 2014/2015. One file contained ONS birth records linked to the HES delivery records and a second file, based on linkage of ONS birth records to HES baby records.The linked data were accessed by researchers from City, University of London in the secure setting of the Virtual Microdata Laboratory facility at ONS. The researchers concerned had ONS Approved Researcher status.The quality of linkage was assessed to ensure that the ONS birth record was linked to the correct delivery record in HES. This involved use of deterministic stepwise rules based on a combination of data items common to both datasets such as place of birth, birthweight, date of birth of the baby, gestational age, multiplicity and sex of baby.A pilot study was carried out using the 2005 data. The file sent to NHS Digital consisted of 617 613 babies who were either born in England or resident in England. The resident in England category was used for births that occurred at home in the ONS linked dataset. NHS Digital first linked these to the HES index to get the HESID and then to the HES delivery records. The file returned to ONS consisted of 624 326 records with a HESID and a second file of 582 963 of ONS birth records that were linked to the HES delivery record. The number of ONS births linked to HESID was higher as it included old and new HESID for some women. This normally happens when a woman is allocated a new HESID and it subsequently becomes evident that she has already been assigned a HESID previously. In addition, there were 25 188 duplicate HES delivery records, that is where the ONS birth record was linked to more than one HES delivery record . By usinAround 66% of the previously linked ONS birth registration and notification records were linked to the HES delivery records in stage 1 of the linkage algorithm shown in Linkage of ONS birth records to HES delivery records for births from 2005 to 2014 showed that the number of records linked using stage 1 of the algorithm increased from 66% in 2005 to 93% in 2014. There was a corresponding decrease in the number of records linked in stage 6 of the algorithm which excludes use of mother’s NHS Number from 29% to 3%.Each year there were about 36 000 duplicate HES delivery records linked to ONS birth records, that is, where the same ONS birth record was linked to multiple HES delivery records. During assessment of the quality of linkage, the HES delivery record with mother’s and baby’s information matching the ONS birth record and with greatest amount of information on onset of labour and method of delivery was retained for analysis. The other records were discarded. In addition, there were 13 300 HES delivery records incorrectly linked to the ONS birth records. It took over 71 days to assess the quality of linkage and to produce a final linked dataset consisting of one ONS birth record linked to the relevant HES delivery record.The baby file was much more straightforward to link than the mother file as it involved a one-to-one link between an ONS birth record and a HES birth record, also referred to as the HES baby record.The numbers of HES birth records linked to ONS birth records, for each year from 2005 to 2014, were higher than the numbers of HES delivery records linked to the ONS birth records see . The quaAlthough the linkage rate increased from 94% in 2005 to 97% in 2014, there were statistically significant differences between distributions of records that were linked to HES delivery records by NHS Digital and those that were not linked in terms of multiplicity, age of mother, ethnicity and region of residence . The linAlthough the data linkage team at NHS Digital has experience of linking external datasets to HES and we used a similar linkage algorithm to that routinely used by NHS Digital to link ONS death records to HES records, there were issues with the quality of linkage. In the period 2005–2014, 2% of HES delivery records were incorrectly linked to the ONS birth records as common data items such as place of birth, date of birth of baby, gestational age, birthweight, multiplicity and sex differed in the HES delivery and ONS birth records. In addition, 366 000 duplicate HES delivery records were linked to ONS birth records. This meant that a considerable amount of time was spent in quality assuring these files.The number of birth registration and notification records linked to the HES delivery records using the NHS Number increased over the years from 2005 to 2014. This was not surprising as completeness of the mother’s NHS Number improved over time in the registration and notification linked records. In 2005, the mother’s NHS Number was present in over two-thirds of the records and this increased to over 90% in 2014. There were also a small proportion of HES records that had the mother’s NHS Number missing. A further quarter of the registration and notification linked records in 2005 were linked using exact date of birth, sex and postcode which reduced to 3% in 2014. There were concerns about using postcode in the linkage algorithm for linking data for earlier years, as the HES index may not hold all historical postcodes of residence of the mother and the postcode on registration and notification linked data was recorded at the time of registration. It is possible the mother could have moved since having the baby and this variable is also subject to recording and reporting errors.Overall, a linkage rate of over 90% was achieved and it improved over time, especially in 2014, when there had been a shorter time before linkage was carried out and HESID would have been less likely to have changed. This suggests that HESID at birth could be retained as a separate field for linkage.Although the linkage rate for ONS birth records to HES births was higher than the linkage rate for the delivery records and we did not assess the quality of linkage, our previous linkage study showed that there were many duplicate HES birth records linked to ONS birth records.NHS Digital acknowledges that a high proportion of baby records are known to be missing in Maternity HES.While ONS birth registration data have remained of consistently high quality, there have been issues with data quality and completeness in Maternity HES.This study shows that it is possible to link a large majority of the linked birth registration and notification records to Maternity HES records, but linkage would be considerably more valuable if data quality and completeness improved in Maternity HES. Information about parity, onset of labour, method of delivery and complications in pregnancy can only be obtained at a national level from Maternity HES, so linking all three national datasets on births and maternity would expand the scope and range of data available.