data come from the kaiser permanente northern california ( kpnc ) diabetes study of northern california ( distance ) , a survey completed from 2005 to 2007 by 20,188 plan members with diabetes . kpnc is a large integrated health - care system that provides comprehensive services to > 3 million members . the sociodemographic characteristics of members are generally representative of the regional population , although the very poor and very wealthy are slightly underrepresented ( 18,19 ) . participants in distance come from the kpnc diabetes registry , which included 174,064 members at the time of sampling . details on distance methods have been described previously ( 20 ) . in brief , distance is a survey cohort study that assesses a range of social and behavioral factors hypothesized to be associated with diabetes - related outcomes ( 20 ) . the survey was based on a random sample ( n = 40,735 ) of the registry stratified by race / ethnicity , with approximately equal samples of african americans , chinese , filipinos , latinos , and non - hispanic whites ( the region s largest ethnic groups ) . the response rate was 62% of eligible respondents and those survey administrators were able to contact . consistent with distance aims around identifying social disparities in diabetes - related outcomes , the current study examined the association between neighborhood deprivation and bmi change . the study area comprised 19 counties that contained or were adjacent to a county containing a kpnc facility and included > 30 distance respondents . of 19,804 participants with a geocodable address in the 19-county area , 18,529 were eligible for the analytic sample based on health - related criteria ( type 2 diabetes ; no pregnancy 9 months before baseline through 9 months after the follow - up measurement ; and no evidence of procedures linked to weight loss , including bariatric surgery or lower - extremity amputation during the study ) . of these , 13,609 were included in the analysis based on availability of bmi measurements , including a baseline measure ( january 1 , 2006march 31 , 2007 ) and a follow - up measure ( january 1 , 2009march 31 , 2010 ) . the average length of time from baseline to follow - up was 3.4 ( sd 0.5 , range 1.84.9 ) years . compared with the study sample , excluded individuals were more likely to be younger , male , non - hispanic white or of other race / ethnicity , never married , less educated , lower income earners , or residents in more - deprived neighborhoods ; differences generally were < 5% between groups . those excluded were also less likely to be taking insulin at baseline or to have started taking insulin during the study , indicating fewer visits to kpnc and , therefore , less opportunity for measurement of bmi . excluded individuals were in worse health based on a comorbidity score ( described later ) . because weight loss is recommended for overweight and obese adults with diabetes , we conducted a sensitivity analysis limited to adults in these bmi categories . bmi measures at baseline and follow - up came from height and weight recorded in kpnc electronic medical records during outpatient visits . for individuals without a clinical measure during the baseline period ( 11% ) , we used bmi calculated from self - reported height and weight from the survey , if available . self - reported and clinical measures at baseline were highly correlated (  = 0.89 ) in those with both measures . this study was approved by the institutional review boards of the kaiser foundation research institute and the university of california , san francisco . outcomes included a continuous measure of bmi change from baseline to follow - up and a discrete measure indicating clinically substantive loss or gain of 7% of baseline bmi or stable bmi ( stable bmi was defined as loss or gain of < 7% of baseline bmi ) . this level of loss has been shown to improve glucose metabolism and reduce insulin resistance ( 21 ) ; although less research has considered the degree of weight gain and diabetes complications , we used a gain of 7% for ease of comparison . hereafter , we refer to these categories as substantive bmi loss , substantive bmi gain , and stable bmi . as an example of substantive bmi change in pounds , a 5-ft 6-in woman with a bmi of 33.0 units ( 204 lb ) at baseline would have lost a minimum of 2.31 bmi units or 14 lb at follow - up . based on previous research ( 10 ) , we created a neighborhood deprivation index ( ndi ) using data from the u.s . we used the 20062010 american community survey rather than the 2000 census given proximity to baseline and follow - up bmi measures . the use of an index rather than single indicators of deprivation may more accurately capture the multidimensional nature of area socioeconomic status ( 10 ) . by including variables representing numerous domains , an index is robust to problems with single variables , such as issues with comparisons over time and place and risk of incomplete conclusions . neighborhood development affects multiple conditions , so an index may have more policy relevance than a single - variable measure . we selected the index used here because it empirically summarizes neighborhood deprivation rather than capturing distinct socioeconomic domains , as with many other summary measures ; was tested in diverse geographies , such as those in the current study ; and provides a standardized and reproducible measure of deprivation ( 6,10 ) . it has been used to study nutrition and physical activity among girls ( 22 ) , perinatal outcomes ( 23 ) , and mortality ( 24 ) . the 2007 home address for each eligible participant was georeferenced using mapmarker version 11 ( pitney bowes ) software . the ndi was generated through principal components analysis of eight variables at the census tract level , including percentages of males in the neighborhood working in management and professional occupations , residents living in crowded housing ( more than one person per room ) , households in poverty , households headed by females with dependents , households receiving public assistance , households earning < $ 30,000 per year , residents 25 years of age with less than a high school education , and residents 16 years of age who are unemployed . the items that comprise the ndi had excellent internal reliability ( cronbach  0.91 ) . principal components analysis found one component with an eigenvalue > 1 that explained 61% of the total variance in the underlying latent construct of neighborhood deprivation . the ndi was based on all census tracts in the 19-county region , regardless of whether a participant lived in the tract , ensuring that tracts were not weighted by the number of participants in the tract . we conducted a sensitivity analysis with a continuous specification of ndi as well as with squared and cubed terms ( neither were significant ) to test for nonlinearity . results for the continuous specification were not substantively different from those from the quartile specification . sociodemographic covariates were age , sex , race / ethnicity , education , income , employment status , nativity , and marital status . although living in a less - deprived neighborhood may result in intentional weight loss because of supports for a healthy lifestyle , neighborhood deprivation may cause unintentional weight change through mechanisms linked to poor health . weight loss is usually considered advantageous to health among adults with diabetes , but unintentional weight loss may indicate deteriorating health ( 25 ) . we assessed mediation by indicators of poor health associated with both unintentional weight loss and neighborhood deprivation , including evidence of cancer any time before baseline or during the study period , very poor glucose control ( measured as at least two consecutive hba1c tests of > 10% ) , and being in the highest decile of comorbidity scores at baseline and halfway through the study period ( 26,27 ) and diabetes duration . the latter two variables were included as controls for poor health not captured by other variables ( 25,2830 ) . the comorbidity score was based on validated inpatient and outpatient adjusters , which predict total health - care utilization across diagnostic clusters ( 27 ) . we also included an indicator of smoking , given its association with neighborhood deprivation and unintentional weight loss ( 32 ) . we were unable to assess mediation by depression , which is associated with both unintentional weight change and neighborhood conditions ( 29,33 ) , for the full sample because of data limitations . however , we conducted a sensitivity analysis that adjusted for depression at the time of the survey for a subsample of respondents . we further adjusted for taking insulin at baseline or starting insulin during the study , given its association with unintentional weight gain and potential link to neighborhood deprivation through mechanisms of self - care and medication adherence ( 34 ) . we adjusted for baseline bmi to address group - level regression to the mean ( i.e. , that higher or lower initial bmi might lead to greater weight loss or gain ) . details of methods used to validate the ndi have been described elsewhere ( 10 ) . we first present distributions of study variables and bivariate comparisons of baseline bmi , mean 3-year change in bmi , and substantive bmi loss or gain by sample characteristics . for bivariate comparisons , we tested for differences using  and t tests . we estimated linear regression models for 3-year bmi change and multinomial logistic regression models of substantive bmi loss or gain , with stable bmi as the reference . for both models , we first specified models that included only the ndi and number of days between bmi measures . we then adjusted for sociodemographics and baseline bmi to assess whether associations were the result of these characteristics rather than of neighborhood deprivation . finally , we adjusted for health indicators linked to unintentional bmi change . we tested for and found no effect modification for ndi and all baseline variables in continuous and discrete models based on a p value of 0.01 to account for multiple tests ; thus , we present only main effects . we specified models with expansion weighting to account for complex sampling design and modified variance estimators to account for neighborhood clustering . we imputed missing values for all baseline and interim exposure variables using multiple imputation ( n = 5 ) . data come from the kaiser permanente northern california ( kpnc ) diabetes study of northern california ( distance ) , a survey completed from 2005 to 2007 by 20,188 plan members with diabetes . kpnc is a large integrated health - care system that provides comprehensive services to > 3 million members . the sociodemographic characteristics of members are generally representative of the regional population , although the very poor and very wealthy are slightly underrepresented ( 18,19 ) . participants in distance come from the kpnc diabetes registry , which included 174,064 members at the time of sampling . details on distance methods have been described previously ( 20 ) . in brief , distance is a survey cohort study that assesses a range of social and behavioral factors hypothesized to be associated with diabetes - related outcomes ( 20 ) . the survey was based on a random sample ( n = 40,735 ) of the registry stratified by race / ethnicity , with approximately equal samples of african americans , chinese , filipinos , latinos , and non - hispanic whites ( the region s largest ethnic groups ) . the response rate was 62% of eligible respondents and those survey administrators were able to contact . consistent with distance aims around identifying social disparities in diabetes - related outcomes , the current study examined the association between neighborhood deprivation and bmi change . the study area comprised 19 counties that contained or were adjacent to a county containing a kpnc facility and included > 30 distance respondents . of 19,804 participants with a geocodable address in the 19-county area , 18,529 were eligible for the analytic sample based on health - related criteria ( type 2 diabetes ; no pregnancy 9 months before baseline through 9 months after the follow - up measurement ; and no evidence of procedures linked to weight loss , including bariatric surgery or lower - extremity amputation during the study ) . of these , 13,609 were included in the analysis based on availability of bmi measurements , including a baseline measure ( january 1 , 2006march 31 , 2007 ) and a follow - up measure ( january 1 , 2009march 31 , 2010 ) . the average length of time from baseline to follow - up was 3.4 ( sd 0.5 , range 1.84.9 ) years . compared with the study sample , excluded individuals were more likely to be younger , male , non - hispanic white or of other race / ethnicity , never married , less educated , lower income earners , or residents in more - deprived neighborhoods ; differences generally were < 5% between groups . those excluded were also less likely to be taking insulin at baseline or to have started taking insulin during the study , indicating fewer visits to kpnc and , therefore , less opportunity for measurement of bmi . excluded individuals were in worse health based on a comorbidity score ( described later ) . because weight loss is recommended for overweight and obese adults with diabetes , we conducted a sensitivity analysis limited to adults in these bmi categories . bmi measures at baseline and follow - up came from height and weight recorded in kpnc electronic medical records during outpatient visits . for individuals without a clinical measure during the baseline period ( 11% ) , we used bmi calculated from self - reported height and weight from the survey , if available . self - reported and clinical measures at baseline were highly correlated (  = 0.89 ) in those with both measures . this study was approved by the institutional review boards of the kaiser foundation research institute and the university of california , san francisco . outcomes included a continuous measure of bmi change from baseline to follow - up and a discrete measure indicating clinically substantive loss or gain of 7% of baseline bmi or stable bmi ( stable bmi was defined as loss or gain of < 7% of baseline bmi ) . this level of loss has been shown to improve glucose metabolism and reduce insulin resistance ( 21 ) ; although less research has considered the degree of weight gain and diabetes complications , we used a gain of 7% for ease of comparison . hereafter , we refer to these categories as substantive bmi loss , substantive bmi gain , and stable bmi . as an example of substantive bmi change in pounds , a 5-ft 6-in woman with a bmi of 33.0 units ( 204 lb ) at baseline would have lost a minimum of 2.31 bmi units or 14 lb at follow - up . based on previous research ( 10 ) , we created a neighborhood deprivation index ( ndi ) using data from the u.s . we used the 20062010 american community survey rather than the 2000 census given proximity to baseline and follow - up bmi measures . the use of an index rather than single indicators of deprivation may more accurately capture the multidimensional nature of area socioeconomic status ( 10 ) . by including variables representing numerous domains , an index is robust to problems with single variables , such as issues with comparisons over time and place and risk of incomplete conclusions . neighborhood development affects multiple conditions , so an index may have more policy relevance than a single - variable measure . we selected the index used here because it empirically summarizes neighborhood deprivation rather than capturing distinct socioeconomic domains , as with many other summary measures ; was tested in diverse geographies , such as those in the current study ; and provides a standardized and reproducible measure of deprivation ( 6,10 ) . it has been used to study nutrition and physical activity among girls ( 22 ) , perinatal outcomes ( 23 ) , and mortality ( 24 ) . the 2007 home address for each eligible participant was georeferenced using mapmarker version 11 ( pitney bowes ) software . the ndi was generated through principal components analysis of eight variables at the census tract level , including percentages of males in the neighborhood working in management and professional occupations , residents living in crowded housing ( more than one person per room ) , households in poverty , households headed by females with dependents , households receiving public assistance , households earning < $ 30,000 per year , residents 25 years of age with less than a high school education , and residents 16 years of age who are unemployed . the items that comprise the ndi had excellent internal reliability ( cronbach  0.91 ) . principal components analysis found one component with an eigenvalue > 1 that explained 61% of the total variance in the underlying latent construct of neighborhood deprivation . the ndi was based on all census tracts in the 19-county region , regardless of whether a participant lived in the tract , ensuring that tracts were not weighted by the number of participants in the tract . we conducted a sensitivity analysis with a continuous specification of ndi as well as with squared and cubed terms ( neither were significant ) to test for nonlinearity . results for the continuous specification were not substantively different from those from the quartile specification . sociodemographic covariates were age , sex , race / ethnicity , education , income , employment status , nativity , and marital status . although living in a less - deprived neighborhood may result in intentional weight loss because of supports for a healthy lifestyle , neighborhood deprivation may cause unintentional weight change through mechanisms linked to poor health . weight loss is usually considered advantageous to health among adults with diabetes , but unintentional weight loss may indicate deteriorating health ( 25 ) . we assessed mediation by indicators of poor health associated with both unintentional weight loss and neighborhood deprivation , including evidence of cancer any time before baseline or during the study period , very poor glucose control ( measured as at least two consecutive hba1c tests of > 10% ) , and being in the highest decile of comorbidity scores at baseline and halfway through the study period ( 26,27 ) and diabetes duration . the latter two variables were included as controls for poor health not captured by other variables ( 25,2830 ) . the comorbidity score was based on validated inpatient and outpatient adjusters , which predict total health - care utilization across diagnostic clusters ( 27 ) . we also included an indicator of smoking , given its association with neighborhood deprivation and unintentional weight loss ( 32 ) . we were unable to assess mediation by depression , which is associated with both unintentional weight change and neighborhood conditions ( 29,33 ) , for the full sample because of data limitations . however , we conducted a sensitivity analysis that adjusted for depression at the time of the survey for a subsample of respondents . we further adjusted for taking insulin at baseline or starting insulin during the study , given its association with unintentional weight gain and potential link to neighborhood deprivation through mechanisms of self - care and medication adherence ( 34 ) . we adjusted for baseline bmi to address group - level regression to the mean ( i.e. , that higher or lower initial bmi might lead to greater weight loss or gain ) . outcomes included a continuous measure of bmi change from baseline to follow - up and a discrete measure indicating clinically substantive loss or gain of 7% of baseline bmi or stable bmi ( stable bmi was defined as loss or gain of < 7% of baseline bmi ) . this level of loss has been shown to improve glucose metabolism and reduce insulin resistance ( 21 ) ; although less research has considered the degree of weight gain and diabetes complications , we used a gain of 7% for ease of comparison . hereafter , we refer to these categories as substantive bmi loss , substantive bmi gain , and stable bmi . as an example of substantive bmi change in pounds , a 5-ft 6-in woman with a bmi of 33.0 units ( 204 lb ) at baseline would have lost a minimum of 2.31 bmi units or 14 lb at follow - up . based on previous research ( 10 ) , we created a neighborhood deprivation index ( ndi ) using data from the u.s . we used the 20062010 american community survey rather than the 2000 census given proximity to baseline and follow - up bmi measures . the use of an index rather than single indicators of deprivation may more accurately capture the multidimensional nature of area socioeconomic status ( 10 ) . by including variables representing numerous domains , an index is robust to problems with single variables , such as issues with comparisons over time and place and risk of incomplete conclusions . neighborhood development affects multiple conditions , so an index may have more policy relevance than a single - variable measure . we selected the index used here because it empirically summarizes neighborhood deprivation rather than capturing distinct socioeconomic domains , as with many other summary measures ; was tested in diverse geographies , such as those in the current study ; and provides a standardized and reproducible measure of deprivation ( 6,10 ) . it has been used to study nutrition and physical activity among girls ( 22 ) , perinatal outcomes ( 23 ) , and mortality ( 24 ) . the 2007 home address for each eligible participant was georeferenced using mapmarker version 11 ( pitney bowes ) software . the ndi was generated through principal components analysis of eight variables at the census tract level , including percentages of males in the neighborhood working in management and professional occupations , residents living in crowded housing ( more than one person per room ) , households in poverty , households headed by females with dependents , households receiving public assistance , households earning < $ 30,000 per year , residents 25 years of age with less than a high school education , and residents 16 years of age who are unemployed . the items that comprise the ndi had excellent internal reliability ( cronbach  0.91 ) . principal components analysis found one component with an eigenvalue > 1 that explained 61% of the total variance in the underlying latent construct of neighborhood deprivation . the ndi was based on all census tracts in the 19-county region , regardless of whether a participant lived in the tract , ensuring that tracts were not weighted by the number of participants in the tract . we conducted a sensitivity analysis with a continuous specification of ndi as well as with squared and cubed terms ( neither were significant ) to test for nonlinearity . results for the continuous specification were not substantively different from those from the quartile specification . sociodemographic covariates were age , sex , race / ethnicity , education , income , employment status , nativity , and marital status . although living in a less - deprived neighborhood may result in intentional weight loss because of supports for a healthy lifestyle , neighborhood deprivation may cause unintentional weight change through mechanisms linked to poor health . weight loss is usually considered advantageous to health among adults with diabetes , but unintentional weight loss may indicate deteriorating health ( 25 ) . we assessed mediation by indicators of poor health associated with both unintentional weight loss and neighborhood deprivation , including evidence of cancer any time before baseline or during the study period , very poor glucose control ( measured as at least two consecutive hba1c tests of > 10% ) , and being in the highest decile of comorbidity scores at baseline and halfway through the study period ( 26,27 ) and diabetes duration . the latter two variables were included as controls for poor health not captured by other variables ( 25,2830 ) . the comorbidity score was based on validated inpatient and outpatient adjusters , which predict total health - care utilization across diagnostic clusters ( 27 ) . we also included an indicator of smoking , given its association with neighborhood deprivation and unintentional weight loss ( 32 ) . we were unable to assess mediation by depression , which is associated with both unintentional weight change and neighborhood conditions ( 29,33 ) , for the full sample because of data limitations . however , we conducted a sensitivity analysis that adjusted for depression at the time of the survey for a subsample of respondents . we further adjusted for taking insulin at baseline or starting insulin during the study , given its association with unintentional weight gain and potential link to neighborhood deprivation through mechanisms of self - care and medication adherence ( 34 ) . we adjusted for baseline bmi to address group - level regression to the mean ( i.e. , that higher or lower initial bmi might lead to greater weight loss or gain ) . details of methods used to validate the ndi have been described elsewhere ( 10 ) . we first present distributions of study variables and bivariate comparisons of baseline bmi , mean 3-year change in bmi , and substantive bmi loss or gain by sample characteristics . for bivariate comparisons , we tested for differences using  and t tests . we estimated linear regression models for 3-year bmi change and multinomial logistic regression models of substantive bmi loss or gain , with stable bmi as the reference . for both models , we first specified models that included only the ndi and number of days between bmi measures . we then adjusted for sociodemographics and baseline bmi to assess whether associations were the result of these characteristics rather than of neighborhood deprivation . we tested for and found no effect modification for ndi and all baseline variables in continuous and discrete models based on a p value of 0.01 to account for multiple tests ; thus , we present only main effects . we specified models with expansion weighting to account for complex sampling design and modified variance estimators to account for neighborhood clustering . we imputed missing values for all baseline and interim exposure variables using multiple imputation ( n = 5 ) . one - fifth were non - hispanic white , african american , or latino , and one - fourth were asian . forty - six percent were working , and approximately one - third ( 29.7% ) were retired . characteristics of study participants , kpnc distance study , 20072008 ( n = 13,609 ) at baseline , 33.7% of participants were overweight and more than one - half ( 52.1% ) were obese . on average , participants experienced minor bmi loss ( 0.12 , sd 3.07 ) ; however , 17.0 and 16.1% experienced substantive ( 7% of baseline ) bmi loss and gain during follow - up , respectively . missing data ( imputed for multivariate models ) was generally < 3% for most variables with the exception of employment ( 12.9% ) and income ( 14.7% ) . mean baseline bmi was higher among participants in more - deprived neighborhoods ( table 1 ) ; differences were statistically significant ( p < 0.001 ) between those living in the least - deprived neighborhoods versus all other quartiles . in contrast , those living in more - deprived neighborhoods were somewhat more likely to have either substantive bmi loss or gain than those living in the least - deprived neighborhoods ; most differences were significant at p < 0.05 . for example , 19.9% of participants living in the most - deprived neighborhoods experienced substantive bmi loss vs. 15.0% in the least - deprived neighborhoods ; similar differences were evident for substantive bmi gain in the most- versus least - deprived neighborhoods ( 18.0 vs. 14.1% ) . mean baseline bmi was significantly associated with most baseline and interim sociodemographic and health - related covariates . mean bmi change was associated with several baseline and interim indicators of health status or behaviors . older , retired , and married adults lost slightly more on average than their younger , employed , and never married counterparts . similar patterns were evident for most cancer indicators , being in poorer health , and having a higher baseline bmi . most sociodemographic and health - related characteristics were also associated with substantive bmi loss or gain relative to maintaining a stable bmi . higher baseline bmi status was associated with significantly greater likelihood of substantive bmi loss or gain . there was no significant difference in 3-year bmi change , measured continuously , by neighborhood deprivation adjusting only for number of days between bmi measures ( table 2 , model 1 ) . after adjusting for sociodemographic characteristics ( table 2 , model 2 ) , there was a significant increase in bmi for adults in the most- versus least - deprived neighborhoods (  = 0.22 , p = 0.02 ) . we further adjusted for health indicators linked to unintentional weight change and found no appreciable differences from the sociodemographic model ( data not shown ) . coefficients for 3-year change in bmi in adults with diabetes , kpnc , 20062010 adults in all three of the more - deprived neighborhood quartiles were more likely to experience substantive bmi loss or gain relative to those in the least - deprived quartile ( table 3 ) . after adjusting for sociodemographic covariates ( table 3 , model 2 ) , a significant difference remained between adults in the least - deprived neighborhood and those in all three more - deprived neighborhood quartiles for substantive bmi loss ( q2 relative risk ratio [ rrr ] 1.19 , 95% ci 1.001.41 ; q3 1.20 , 1.021.42 ; q4 1.30 , 1.081.55 ) or substantive bmi gain ( q2 1.25 , 1.041.49 ; q3 1.24 , 1.041.49 ; q4 1.45 , 1.201.75 ) . results were similar after adjustment for health indicators linked to unintentional weight change ( data not shown ) . rrrs from multinomial logistic regression models of 3-year loss or gain of 7% of baseline bmi in adults with diabetes , kpnc , 20062010 results were also similar when the analysis was limited to overweight and obese participants and those with depression ( data not shown ) . there was no significant difference in 3-year bmi change , measured continuously , by neighborhood deprivation adjusting only for number of days between bmi measures ( table 2 , model 1 ) . after adjusting for sociodemographic characteristics ( table 2 , model 2 ) , there was a significant increase in bmi for adults in the most- versus least - deprived neighborhoods (  = 0.22 , p = 0.02 ) . we further adjusted for health indicators linked to unintentional weight change and found no appreciable differences from the sociodemographic model ( data not shown ) . coefficients for 3-year change in bmi in adults with diabetes , kpnc , 20062010 adults in all three of the more - deprived neighborhood quartiles were more likely to experience substantive bmi loss or gain relative to those in the least - deprived quartile ( table 3 ) . after adjusting for sociodemographic covariates ( table 3 , model 2 ) , a significant difference remained between adults in the least - deprived neighborhood and those in all three more - deprived neighborhood quartiles for substantive bmi loss ( q2 relative risk ratio [ rrr ] 1.19 , 95% ci 1.001.41 ; q3 1.20 , 1.021.42 ; q4 1.30 , 1.081.55 ) or substantive bmi gain ( q2 1.25 , 1.041.49 ; q3 1.24 , 1.041.49 ; q4 1.45 , 1.201.75 ) . results were similar after adjustment for health indicators linked to unintentional weight change ( data not shown ) . rrrs from multinomial logistic regression models of 3-year loss or gain of 7% of baseline bmi in adults with diabetes , kpnc , 20062010 results were also similar when the analysis was limited to overweight and obese participants and those with depression ( data not shown ) . in a diverse population of adults with type 2 diabetes in a managed - care setting , the ndi was modestly associated with both substantive bmi loss and gain ( 7% loss or gain of baseline bmi ) over an 3-year period . for example , 18.0% of adults in the most - deprived quartile of neighborhood deprivation experienced substantive bmi gain vs. 16.1% in q2 and q3 and 14.1% in q1 ( least deprived ) . after adjustment for sociodemographic characteristics and health indicators associated with unintentional weight change , significant differences remained between the more - deprived quartiles and the least - deprived quartile for substantive bmi loss and gain . neighborhood deprivation was positively associated with bmi change measured as a simple difference between baseline and follow - up . we tested for and found no substantive effect modification by sociodemographic variables , health behavioral or health status variables , duration of diabetes , or baseline bmi ; more research is needed to assess other characteristics not measured in this study . the results highlight the importance of evaluating segmented relationships ( allowing for simultaneous associations with both gain and loss ) as well as continuous or repeated measures of weight change with regard to neighborhood deprivation in future studies . failure to do so may partly explain the mixed results from the limited number of longitudinal studies of weight change to date , with one investigation finding no association between neighborhood socioeconomic status and repeated bmi measures and two others finding positive associations with a continuous measure of difference ( 68 ) . studies that assess only clinically substantive weight loss measured as a discrete variable may similarly miss important relationships between weight gain and neighborhood characteristics . investigations of weight trajectories among adults with diabetes confirmed that these individuals experience a range of outcomes from weight loss to gain , stability , and fluctuations , with gain followed by loss and vice versa ( 11,17 ) . although we did not examine fluctuations because of the short follow - up time , a more complex approach may capture possible shape - variant rather than linear relationships between neighborhood deprivation and weight change over time and provide guidance for practice and policy . given the mixed results in longitudinal research on neighborhood deprivation and weight change , such approaches may be important to assessing implications of moving to deprived neighborhoods for bmi change as well as associated policy implications . in fact , weight fluctuation may explain the observed association between neighborhood deprivation and either substantive bmi loss or gain . adults with diabetes living in more - deprived neighborhoods may be subject to greater fluctuation than adults in less - deprived settings ; for example , they may be more likely to lose weight given their initially higher average bmis but have more difficulty maintaining weight loss because of an environment unsupportive of weight maintenance [ i.e. , with poorer walkability ; fewer places to exercise ( 1 ) ; limited availability of fruits and vegetables ( 2 ) ; and greater exposure to environmental stressors , such as noise and violence ( 3,4 ) ] , which is linked to weight gain . for similar reasons , some residents may also be more likely to gain , followed by a greater need to lose weight . the net loss observed among residents in more - deprived neighborhoods the same may be true of bmi gain , although notably , this association is consistent with research suggesting that conditions in more - deprived neighborhoods are not conducive to avoiding gain ( 2,4,3537 ) . research that captures an even wider spectrum of patterns of bmi change than considered here may be useful in understanding associations with neighborhood conditions . this may be especially important for adults with weight - related conditions like diabetes , most of whom are engaged in a long - term process of weight management in an attempt to delay disease complications ( 14 ) . the observed association between greater neighborhood deprivation and substantive bmi loss or gain may instead be a result of mechanisms related to poor health ; that is , bmi change may have been unintentional . for example , neighborhood deprivation is associated with a number of health conditions associated with unintentional weight change ( either directly or through medications ) , such as depression and disability , as well as with health behaviors linked to unintentional weight change , like smoking . we adjusted for health conditions , behaviors , and metabolic status associated with unintentional weight change , but these controls may have been inadequate . further , our control for baseline bmi may not have fully adjusted for regression to the mean among adults living in more - deprived neighborhoods , who had higher initial body weight . we adjusted for higher baseline bmi , which may be a proxy for greater motivation to lose weight , but we were unable to adjust directly for motivation to lose weight , which could potentially disproportionately affect residents in more - deprived settings . additional research is needed to identify whether weight loss in adults with diabetes in relation to neighborhood deprivation is intentional ( motivation to lose weight ) or unintentional ( health conditions ) as well as to identify physiological pathways to weight change . the sample was drawn from a managed - care population of adults with diabetes ; therefore , external validity may be limited to others in a similar managed - care setting . although neighborhood deprivation and bmi may differ for managed - care members with diabetes compared with uninsured patients or those insured by different organizations , observed exposure - outcome associations are typically much less variable across populations . the relatively low response rate of 62% may have resulted in selection bias linked to neighborhood deprivation and may mean that patterns of bmi across neighborhoods are somewhat biased , but it is less likely that associations were biased . similarly , survey respondents in more - deprived neighborhoods were more likely to be excluded from the sample because of missing bmi measurements at baseline or follow - up , which may have also resulted in selection bias , although we assume it to be minor . census tracts , used as a proxy for neighborhoods , may not capture neighborhood boundaries in ways that are most meaningful for residents or for health . the findings based on census tracts likely underestimate true associations related to contextually defined ( real ) neighborhoods that have substantive deprivation boundaries and transition zones . overall , the findings point to the importance of evaluating segmented relationships ( allowing for simultaneous associations with both gain and loss ) in longitudinal studies of neighborhood deprivation and weight change . evaluating segmented relationships may be particularly key for studies involving patients with diabetes in whom both weight loss and weight gain , intentional and unintentional , have important health implications ( 1217,38 ) .
objectiveto compare associations between neighborhood deprivation and measures of bmi change among adults with type 2 diabetes.research design and methodsusing data from the kaiser permanente diabetes study of northern california ( distance ) survey , we estimated the association between neighborhood deprivation and two measures of bmi change over 3 years : 1 ) a continuous measure and 2 ) a categorical measure of clinically substantive bmi loss or gain ( 7% of baseline bmi ) versus stable bmi . 
 the sample included 13,609 adults.resultson average , there was little change in bmi ( 0.12 , sd 3.07 ) ; 17.0 and 16.1% had clinically substantive bmi loss or gain , respectively , at follow - up . 
 there was a positive association between neighborhood deprivation and bmi change for adults in the most versus least - deprived quartile of neighborhood deprivation (  = 0.22 , p = 0.02 ) in adjusted models . 
 in addition , relative to the least - deprived quartile ( q1 ) , adults in more - deprived quartiles of neighborhood deprivation were more likely to experience either substantive bmi loss ( q2 relative risk ratio 1.19 , 95% ci 1.001.41 ; q3 1.20 , 1.021.42 ; q4 1.30 , 1.081.55 ) or gain ( q2 1.25 , 1.041.49 ; q3 1.24 , 1.041.49 ; q4 1.45 , 1.201.75).conclusionsgreater neighborhood deprivation was positively associated with bmi change among adults with diabetes as well as with clinically substantive bmi loss or gain . 
 findings stress the importance of allowing for simultaneous associations with both gain and loss in future longitudinal studies of neighborhood deprivation and weight change , which may be particularly true for studies of patients with diabetes for whom both weight loss and gain have health implications .